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Two Years in the Life of a University Hospital Tobacco Cessation Service: Recommendations for Improving the Quality of Referrals
The Joint Commission Journal on Quality and Patient Safety Performance Measures
Two Years in the Life of a University Hospital Tobacco Cessation Service: Recommendations for Improving the Quality of Referrals Wendy G. Bjornson, MPH; David H. Gonzales, PhD; Catherine J. Markin, MD; Noal Clemons, BA; Frances Favela, MPH; Trisha M. Coleman, MA; Caroline Koudelka, MPH; Jodi A. Lapidus, PhD
H
ospitalization provides an opportunity to offer tobacco cessation services for patients who otherwise might not seek help; because of illness, these patients may be more receptive to quitting.1 The effectiveness of initiating evidence-based treatment during this “teachable moment,” particularly for cardiac patients, has been reported in previous studies.2–6 Tobacco cessation services during hospitalization as a potential strategy for improving health outcomes and reducing costs is gaining momentum.7 The Joint Commission now includes four inpatient tobacco treatment core measures.8 The US Public Health Service Guideline, Treating Tobacco Use and Dependence: 2008 Update (PHS Guideline), includes recommendations for hospital patients and administrators.9 Finally, the Centers for Medicaid & Medicare Services (CMS) has established an incentive program for phasing in “meaningful use” of electronic health records (EHRs), with documentation for treatment of tobacco use among the core objectives.10,11 As more hospitals in the United States implement tobacco treatment services for all patients and incorporate them into EHR systems, additional guidance will be needed. Design and implementation guidelines for hospital services are described in detail elsewhere.12–16 These services generally follow the PHS Guideline as translated to hospital systems. The Joint Commission measures are designed to evaluate implementation of these guidelines in hospitals and provide a basis for quality improvement (QI). These measures evaluate the provision of (1) tobacco use screening for all patients, (2) tobacco use treatment (counseling and medications) during the hospital stay, (3) tobacco use treatment offered to sustain abstinence after discharge, and (4) follow-up coaching/counseling after discharge.8* Hospital-based tobacco cessation services have been shown to be effective only when all treatment steps are completed.2 * TOB-4, Tobacco Use: Assessing Status After Discharge, requires a follow-up call to the patient during the 30 days after discharge to assess tobacco use status, adherence to cessation medication, and continued participation in counseling. TOB-4 has been temporarily suspended since January 1, 2015, because of feasibility of data collection.
May 2016
Article-at-a-Glance Background: Hospitalization, when patients may be more
receptive to quitting, provides an opportunity to provide tobacco cessation services for patients who otherwise might not seek help. Although specialized tobacco cessation services are shown to be effective if evidence-based treatment, including follow-up, is completed, resources are limited and guidelines are needed, and few smokers complete all treatment steps. Experience drawn from an analysis of two-year implementation data from the Oregon Health & Science University (OHSU) Tobacco Cessation Consult Service is presented. Methods: Data for 5,827 smokers discharged from OHSU University hospital between January 2011 and December 2012 were analyzed to determine patient characteristics and identify predictors of completing each of four treatment steps: consult ordered, consult completed, follow-up arranged, and follow-up completed. Results: Smokers were younger and male (p < 0.0001) and significantly different with respect to insurance class, admission type, history of mental disorders, primary discharge diagnoses, and length of stay (p < 0.0001) than nonsmokers. Predictors of having a tobacco consult order were admission for elective medical procedures; orders for medications to treat withdrawal; history of mental health/substance use disorders; primary diagnoses of cardiovascular, endocrine, gastrointestinal, or pulmonary disease; and longer hospitalizations. Smokers admitted through the emergency department had the lowest rates of follow-up completion and abstinence. Admission for an elective surgery was the only predictor of completing all treatment steps through followup (p ≤ 0.05). Conclusions: This study adds important information about how hospitalized smokers respond to each step of tobacco treatment in a real-world setting and offers strategies for improving referrals.
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The Joint Commission Journal on Quality and Patient Safety There are limited published data on implementation of these services across all hospital patients and on what proportion of patients completed all treatment steps, including follow-up. Some published studies report results only for specific patient (such as cardiac) populations.3–6 We are aware of only two published studies that report on implementation for all patients.17,18 In one of these studies, 58.3% of the treatment group and 43.0% of the nontreatment group completed all treatment steps and atients were contacted for follow-up.17 In the other study, of all p identified as tobacco users, 18.1% were referred for cessation services. In-hospital treatment was completed for 68.1% of those referred, and 38.2% of treated patients were reached for follow-up (about 5% of the total population of smokers).18 Some of the reasons for failure to complete follow-up were deceased; resided at a nursing home; refused; too sick to compete interview; and, most frequently, lost to follow-up. In both of these studies, staffing was limited to ≤ 1.0 full-time equivalent (FTE), which limited patient reach. Unlike those two studies, which did not use exclusion criteria for selecting patients and instead offered services to “all comers,” another study’s exclusion criteria, which include a history of drug or alcohol addiction, cancer, depression or psychiatric illness, and inability to attend follow-up, eliminated more than 70% of initially eligible patients.19 On the basis of limited published data, then, an all-comers selection strategy results in significant attrition at each step, with few patients receiving complete evidence-based treatment. Yet application of strict exclusion criteria, unsurprisingly, eliminates many patients. Further, when hospital budgets limit funding there is insufficient staff to reach the many patients who may need services. A more strategic approach is needed to improve the effectiveness of program services despite limited staff and help improve outcomes. We are not aware of any published reports that address how outcomes of hospital-based tobacco cessation services might be improved. In this article, we describe and report results for a tobacco cessation consult service at a large academic medical center. We had observed that some of our medical teams were not referring many patients, while others were referring many patients who could not complete treatment. We wanted to develop strategies to improve the quality of medical team referrals and the overall effectiveness of our services within the constraints of our limited resources. Our purpose for the analysis was twofold: (1) to compare smokers versus nonsmokers and smokers with and without consult orders admitted to our hospital and discharged between January 2011 and December
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2012, and (2) to identify predictors for moving through each step of tobacco cessation services provided for our patients.
Methods Setting Oregon Health & Science University (OHSU), located in Portland, Oregon, is a 543-bed, Level 1 trauma and teaching hospital that admits nearly 23,000 adult patients annually. OHSU implemented a Tobacco Cessation Consult Service (Consult Service) for hospital patients in 2007 in conjunction with a 100% tobacco-free campus policy. The Consult Service was implemented (including staff training) across all units and was available to any patient who reported smoking at admission. The Consult Service is managed through the OHSU Smoking Cessation Center (SCC).
Procedures Patients were referred to the Consult Service by physicians and nurses through the EHR. Smoking status was assessed at admission, orders for stop-smoking medications were entered into the EHR, and orders for a bedside consult were placed on the Consult Service patient list. A master’s-level-trained tobacco treatment specialist (TTS) first reviewed the list and patients’ medical records, initiated consult documentation, and then attempted at least three times within 48 hours to see each patient for a bedside consult.
Tobacco Cessation Consult Protocol The consult followed a common protocol, which consisted of an assessment of nicotine withdrawal symptoms, current medication orders, present use of any tobacco cessation medications, and ongoing use and dose of medications on the basis of withdrawal symptoms. The TTS completed a tobacco use history/assessment, determined willingness of the patient to remain abstinent after discharge, discussed a discharge plan including cessation medications, and asked permission to follow up by phone two weeks postdischarge. A consult was considered complete if the TTS was able to evaluate withdrawal, determine willingness to discuss smoking, and provide tobacco cessation information. The TTS then entered notes into the EHR and contacted the medical team with an update. For patients unwilling or unable to have a discussion, the TTS arranged to come back another time, leaving written information for later use. All patients seen by the TTS received OHSU–tailored patient information about quitting tobacco use, including con regon Tobacco Quit Line, and were tact information for the O 20 encouraged to call.
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The Joint Commission Journal on Quality and Patient Safety After a consult was completed, the TTS entered information into the SCC administrative database. Patients giving permission for follow-up were flagged, and up to three contact attempts were made, beginning two weeks after discharge. When contacted, the TTS determined smoking status and use of programs and medications, answered questions, provided encouragement, made additional recommendations, and entered the follow-up data. For patients requesting more help, an additional contact was offered. Limited resources prevented additional follow-up for all patients.
Research Questions Our analysis was designed to address the following questions: 1. Are there differences between characteristics of OHSU smokers versus nonsmokers and smokers with and without consult referrals? Do these differences help explain outcomes? 2. How do smokers move through the cessation treatment steps offered at OHSU? 3. What characteristics predict completing each treatment step? 4. What characteristics predict completing all treatment steps? 5. What QI changes are suggested?
Database Data stored in the SCC database were combined with selected and Institutional Review Board–approved data from the OHSU Research Data Warehouse (RDW). The variables for all patients were as follows: age, sex, type of admission (emergent, trauma, urgent medical, elective medical, urgent surgical, elective surgical), type of insurance coverage (or no coverage), any history of mental disorders (including addictions), length of stay, length of consult, and the primary and up to three additional discharge diagnoses (International Statistical Classification of Diseases and Related Health Problems, Ninth Revision [ICD–9] codes). Variables for smokers included smoking cessation medication orders while hospitalized. For smokers seen for a consult, variables included the length of the consult and, if reached by phone, self-reported smoking status (smoking more, less, or abstinent since discharge) at the two-week follow-up call. These variables were chosen because they were predictive in other studies (for example, history of mental disorders,9,21 discharge diagnosis2,18,22) or because clinical observations suggested they (for example, type of admission) might be predictive for the OHSU population.
Statistical Methods Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina). Significance tests for May 2016
comparisons of current smokers versus current nonsmokers reported in Table 1 (page 212) were performed by t-test for the continuous variable age (normal distribution), Wilcoxon ranksum test for the continuous variable length of stay (skewed distribution), and by chi-square test for binary variables sex, history of mental disorders, and orders for cessation medications. For categorical variables with more than two groups (insurance class, admission type, and primary discharge diagnosis), chi-square tests were performed on each variable level versus all other combined, and p values were adjusted for multiple comparisons by false discovery rate method.23 Chi-square tests (categorical variables) and t-tests (continuous variables) were used to determine p values to compare proportions and means of demographic variables between elective surgical patients and all other patient types combined, as reported in Appendix 2.
Logistic Regression Analysis Odds ratios (ORs) for predictors of subject participation or inclusion at each measurement step (consult referral, consult completed, follow-up arranged, follow-up completed [reported in Appendix 1]) were determined using multivariate logistic regression models generated for each step. Regression models for all steps used independent variables gender, age, insurance type, history of mental disorders, time spent at patient contact, length of hospital stay, smoking cessation medications given in hospital, smoking cessation medications prescribed at discharge, and any of various diagnoses (cardiovascular disease, pulmonary disease, cancer, endocrine disorder, injury, orthopedic, gastrointestinal, and genitourinary) to predict subject inclusion. Three independent variables were used selectively in the following measurement step models: (1) “medication orders” was removed from the consult referral model because they were typically done together, (2) “time spent” was included only for the “follow-up arranged” and “follow-up completed” models, and (3) “medications at discharge” was included only for the “follow-up completed” model. Models were developed via the backward elimination method, whereby variables not found to be significant predictors of subject inclusion (p > 0.10) were removed from the model.
Results Patient Characteristics Table 1 compares characteristics of current smokers versus current nonsmokers/former smokers and smokers with and without consult referrals to provide an overview of the smoking population and the subgroup referred for treatment. Compared Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety Table 1. Population Comparisons: Non/Former Smokers Versus Smokers and Smokers With and Without Consult Orders* Non/Former Smokers No. (%) 22,438 Average Age (at admission) Gender Male
79.4
Smokers No. (%) 5,827
52
p Value†
20.6 47
Smokers Without Consult Orders No. (%) 3,752
< 0.0001
2,075
46
p Value†
35.6 49
< 0.0001
9,271
41.3
3,283
56.3
13,167
58.7
2,544
43.7
Commercial/Contracts
8,955
39.9
1,323
22.7
< 0.0001
928
24.7
395
19.1
Medicaid
3,590
16.0
1,675
28.8
< 0.0001
1,059
28.2
616
29.7
0.2807
Medicare
7,836
34.9
1,536
26.4
< 0.0001
899
24.0
637
30.7
< 0.0001
Nonsponsored
1,119
5.0
904
15.5
< 0.0001
588
15.7
316
15.3
0.6712
Other/unknown sponsored
938
4.2
386
6.6
< 0.0001
278
7.4
108
5.2
0.0021
5,092
22.7
2,308
39.6
< 0.0001
1,525
40.6
783
37.8
0.0329
603
2.7
344
5.9
< 0.0001
272
7.3
72
3.5
< 0.0001
6,271
28.0
1,228
21.1
< 0.0001
676
18.0
552
26.6
< 0.0001
884
3.9
134
2.3
< 0.0001
56
1.5
78
3.8
< 0.0001 0.0013
Female
< 0.0001
64.4
Smokers With Consult Orders No. (%)
2,096
55.9
1,187
57.2
1,656
44.1
888
42.8
0.3229
Insurance Class < 0.0001
Admission Type Emergent Trauma Urgent medical Elective medical Urgent surgical
1,154
5.1
325
5.6
0.1818
182
4.9
143
6.9
Elective surgical
7,906
35.2
1,394
23.9
< 0.0001
950
25.3
444
21.4
0.0012
528
2.4
91
1.6
0.0003
91
2.4
0
0.0
< 0.0001
6,691
29.8
3,974
68.6
< 0.0001
2,445
65.2
1,529
74.8
< 0.0001
3.8
2,633
45.2
< 0.0001
1,331
35.5
1,302
62.8
< 0.0001
3,192
14.2
556
9.5
< 0.0001
360
9.6
196
9.4
0.8528
850
3.8
155
2.7
< 0.0001
82
2.2
73
3.5
0.0037
2,619
11.7
837
14.4
< 0.0001
323
8.6
514
24.8
< 0.0001
Mental disorders
485
2.2
570
9.8
< 0.0001
525
14.0
45
2.2
< 0.0001
Pulmonary
582
2.6
221
3.8
< 0.0001
86
2.3
135
6.5
< 0.0001
1,690
7.5
481
8.3
0.0649
291
7.8
190
9.2
0.0808 0.8104
Other History of Mental Disorders Yes
Cessation Medication Orders Yes
861
Primary Discharge Diagnosis Cancer Endocrine Cardiovascular
Gastrointestinal Genitourinary
836
3.7
179
3.1
0.0189
113
3.0
66
3.2
Orthopedics
2,179
9.7
445
7.6
< 0.0001
325
8.7
120
5.8
0.0001
Injury
2,917
13.0
1,117
19.2
< 0.0001
830
22.1
287
13.8
< 0.0001
Other
6,927
31.1
1,194
20.8
779
21.0
415
20.3
Length of Stay Mean (SD)
4.70 (5.71)
5.20 (7.39)
0.0029†
4.70 (6.24)
6.11 (9.01)
< 0.0001‡
SD, standard deviation. * Discrepancies in column totals for Insurance Class, Admission Type, History of Mental Disorders, and Primary Discharge Diagnosis are due to missing values. Insurance Class and Admission Type has 3 missing values for smokers and smokers with consult orders; History of Mental Disorders has 31 missing values for smokers and smokers with consult orders; Primary Discharge Diagnosis has 161 missing values for nonsmokers, 72 missing values for smokers, 38 missing values for smokers without consult orders, and 34 missing values for smokers with consult orders. † p values determined by t-test for continuous variable age and by chi-square test for binary variables sex, history of mental disorders, and orders for cessation medications. For categorical variables—insurance class, admission type, and primary discharge diagnosis—chi-square tests were performed on each variable level versus all others combined and adjusted for multiple comparisons by the false discovery rate method. ‡
Wilcoxon rank-sum test (nonparametric) was used for length of stay to compare skewed (nonnormal) distributions.
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The Joint Commission Journal on Quality and Patient Safety to nonsmokers/former smokers, current smokers were significantly more likely to be younger, male, Medicaid-eligible or uninsured, and admitted through the emergency department (ED) or trauma unit, and they were more than twice as likely to have a history of mental disorders (p < 0.0001). Current smokers were also significantly more likely to have a primary discharge diagnosis of cardiovascular disease, mental disorders, pulmonary disease, or injuries (p < 0.0001). Smokers with consult referrals were more likely to be older; male; covered by commercial insurance and Medicare; admitted for urgent and elective medical procedures and urgent surgical procedures; and have a history of mental disorders, endocrine, cardiovascular, and pulmonary discharge diagnoses, and longer hospitalizations.
Patient Flow From January 2011 through December 2012, 5,827 current smokers were discharged from the OHSU hospital, 2,075 (35.6%) of whom were referred for a tobacco cessation consult. Of those patients referred, 1,683 (81.1%) consults were completed. “Patient discharged early” was the primary reason for consults not completed. Among patients with completed consults, nearly half (47.2%) were not scheduled for a follow-up call. Typically, these patients had no phone or were being discharged to a facility without private-phone access (for example, prison, rehabilitation/care facilities). Of patients agreeing to be called, 613 (69.0%) were reached (36.4% of all patients with completed consults and 10.5% of all smokers). Of those reached, 47.0% reported abstinence (4.9% overall), 42.6% reported reduced smoking, and 10.4% reported relapse to smoking > levels before admission (Figure 1, right).
Attrition from Referral to Follow-Up Table 2 (page 214) shows the proportion of total smokers referred for a consult by selected variables and rates of follow-up completion at each step, with smokers remaining at the previous step as the denominator. The composition of patients at each step changes and can be determined by comparison of results within subgroups of variables. For example, Medicare patients had proportionately higher rates of referrals, while commercially insured patients had proportionately higher rates of follow-up completion.
Predictors of Completion of Treatment Steps Appendix 1 (available in online article) shows ORs, confidence intervals (CIs), and p values of treatment-step completion by patient characteristics; we now describe the results. May 2016
Patient Flow Total current smokers discharged Jan 2011–Dec 2012 N = 5,827
Current smokers with unique orders for tobacco consult services Jan 2011–Dec 2012 N = 2,075
Consults completed N = 1,683
Arrange follow-up call N = 888
Patient reached N = 613
Abstinent n = 288 (47.0%)
Reduced n = 261 (42.6%)
Current smokers without consult orders N = 3,752 (64.4%) Incomplete n = 392 (18.9%) • Discharged early n = 261 • Canceled orders n = 49 • Clinically unable to see n = 36 • Unavailable with 3+ attempts n = 18 • Patient refused n = 13 • Other n = 15 No follow-up call n = 795 (47.2%) • Not appropriate (e.g., d/c to prison, nursing home, rehab; no phone) n = 463 • Declined n = 321 • Forgot to offer n = 11 Not reached n = 275 (31.0%) • Not reached after 3+ attempts n = 174 • Not at given number n = 67 • In treatment facility n = 5 • Deaths n = 4 • Other n = 25 Relapsed ≥ previous use n = 64 (10.4%)
Figure 1. The patient flow, starting with a total of 5,827 current smokers discharged from January 2011 through December 2012, is shown. d/c, discharged.
Step 1. Consult Referral. Patients significantly more likely to be referred for a consult were those who were admitted for an elective medical procedure; had a history of mental disorders; were covered by Medicaid or Medicare; had a primary diagnosis of cardiovascular disease, endocrine disorder, gastrointestinal disorder, or pulmonary disease; and/or had incrementally longer hospital stays. Patients least likely to be referred for a consult were those admitted for trauma and those with a primary diagnosis of an injury or mental disorder. Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety Table 2. Number and Percentage of Smokers at Each Treatment Step by Subgroups (Smokers Included in Previous Step as Denominator)*
All
Total Smokers
1. Consult Referral
2. Consult Completion
3. Follow-Up Arranged
4. Follow-Up Completion
n
n (%)
n (%)
n (%)
n (%)
n (%)
2,075 (35.6)
1,683 (81.1)
888 (52.8)
613 (69.0)
288 (47.0)
5,827
Sex
Abstinent
Female
2,544
888 (34.9)
740 (83.3)
413 (55.8)
279 (67.6)
123 (44.1)
Male
3,283
1,187 (36.2)
943 (79.4)
475 (50.4)
334 (70.3)
165 (49.4)
Insurance Class† Commercial/Contracts
1,323
395 (29.9)
327 (82.8)
172 (52.6)
129 (75.0)
70 (54.3)
Medicaid
1,675
616 (36.8)
495 (80.4)
264 (53.3)
177 (67.0)
74 (41.8)
Medicare
1,536
637 (41.5)
522 (81.9)
278 (53.3)
195 (70.1)
91 (46.7)
Nonsponsored
904
316 (35.0)
254 (80.4)
135 (53.1)
92 (68.1)
40 (43.5)
Other
386
108 (28.0)
83 (76.9)
37 (44.6)
20 (54.1)
13 (65.0)
Emergent
2,308
783 (33.9)
609 (77.8)
266 (43.7)
175 (65.8)
58 (33.1)
Admission Type‡
Trauma
344
72 (20.9)
54 (75.0)
29 (53.7)
16 (55.2)
8 (50.0)
Urgent medical
1,228
552 (45.0)
466 (84.4)
263 (56.4)
169 (64.3)
98 (58.0)
Elective medical
134
78 (58.2)
66 (84.6)
46 (69.7)
37 (80.4)
12 (32.4)
Urgent surgical
325
143 (44.0)
114 (79.7)
68 (59.6)
43 (63.2)
25 (58.1)
Elective surgical
1,394
444 (31.9)
372 (83.8)
214 (57.5)
173 (80.8)
87 (50.3)
History of Mental Disorders
3,974
1,529 (38.5)
1,257 (82.2)
661 (52.6)
462 (69.9)
207 (44.8)
Cessation Medication Orders
2,633
1,302 (49.4)
1,105 (84.9)
639 (57.8)
437 (68.4)
184 (42.1)
Primary Discharge Diagnosis Cancer
556
196 (35.3)
168 (85.7)
102 (60.7)
78 (76.5)
45 (57.7)
Endocrine
155
73 (47.1)
64 (87.7)
37 (57.8)
22 (59.5)
9 (40.9)
Cardiovascular
837
514 (61.4)
441 (85.8)
265 (60.1)
191 (72.1)
122 (63.9)
Mental disorders
570
45 (7.9)
33 (73.3)
14 (42.4)
8 (57.1)
0 (0.0)
Pulmonary
221
135 (61.1)
112 (83.0)
51 (45.5)
36 (70.6)
11 (30.6)
Gastrointestinal
481
190 (39.5)
146 (76.8)
86 (58.9)
57 (66.3)
21 (36.8)
Genitourinary
179
66 (36.9)
50 (75.8)
26 (52.0)
16 (61.5)
4 (25.0)
Orthopedics
445
120 (27.0)
92 (76.7)
41 (44.6)
31 (75.6)
12 (38.7)
Injury
1,117
287 (25.7)
226 (78.7)
95 (42.0)
62 (65.3)
28 (45.2)
Other
1,194
415 (34.8)
321 (77.3)
Prescription at Discharge§ Consult Time (minutes)II
157 (48.9)
105 (66.9)
29 (27.6)
360 (65.6)
242 (67.2)
112 (46.3)
600
NA
NA
1–5
360
NA
NA
75 (21.7)
48 (64.0)
18 (37.5)
6–10
774
NA
NA
426 (55.3)
279 (65.5)
124 (44.4)
11–15
389
NA
NA
251 (64.5)
183 (72.9)
92 (50.3)
> 15
170
NA
NA
129 (75.9)
98 (76.0)
51 (52.0)
6.1 (9.1)
6.2 (6.1)
6.4 (6.3)
6.1 (5.7)
7.4 (7.0)
Length of stay mean (SD)
5.2 (7.4)
NA, not appropriate; SD, standard deviation. * Discrepancies in column totals are due to missing values, resulting in changes in denominators. †
Three missing values.
‡
Three missing values and deletion of 91 smokers in the Other category (as shown in Table 1), none of whom had had a consult ordered.
§
Fifty-one smokers with a discharge order did not complete a consult and were removed from the denominator.
II
Five smokers in category 1–5 minutes did not complete a consult and were removed from the denominator. Three smokers in category 6–10 minutes did not complete a consult and were removed from the denominator.
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The Joint Commission Journal on Quality and Patient Safety Step 2. Consult Completion. Patients significantly more likely to have completed consults were those admitted for elective surgery and those with medication orders. There were marginally significant differences for female patients and for patients admitted for urgent medical procedures and cardiovascular disease. Step 3. Follow-Up Arranged. Female patients were significantly more likely to have a follow-up call arranged, as were patients with any admission type other than through the ED and those who had stop-smoking medications prescribed at discharge, longer tobacco cessation consultations, and/or a primary discharge diagnosis of cardiovascular disease. Patients significantly less likely to have follow-up arranged had the briefest consult times or were hospitalized for injuries. Step 4. Follow-Up Completion. Patients significantly more likely to complete follow-up were those admitted for elective surgical procedures. There was a trend toward significance for patients who had longer tobacco cessation consultations.
Characteristics of Smokers Completing All Treatment Steps As shown by the logistic regression analysis, smokers most likely to complete all treatment steps were those admitted for elective surgeries. Appendix 2 compares characteristics of smokers from each portal of admission. Smokers admitted for elective surgeries had more equal proportions of male and female patients and were much less likely to have medication orders or to have a history of mental disorders. More than half of smokers admitted for elective surgeries had discharge diagnoses of either cancer or orthopedic procedures, and more than 25% had cardiovascular conditions, gastrointestinal conditions, or injuries. Smokers completing all treatment steps also had lower rates of Medicaid and nonsponsored (uninsured) insurance coverage, were older, and had shorter hospital stays. Smokers admitted through the ED were almost twice as likely to be male, half had orders for medications to treat withdrawal, more than three quarters had a history of mental disorders, and more than 20% had a primary diagnosis of mental disorders, while nearly 20% had injuries. These patients also had the highest rates of nonsponsored coverage.
Discussion Tobacco cessation is necessary for improving many health outcomes, and providing evidence-based treatment for tobacco cessation during hospitalization is important for health care QI. The potential benefits of hospital-based services are well recognized, leading some to advocate for making services for all hosMay 2016
pitalized smokers the official “default” position.24 We undertook our data analysis after five years of implementation to help improve outcomes of our consult service. Similarly to findings of previous studies, we found that only a small proportion of smokers received all steps of evidence-based treatment. Of all smokers identified at admission at OHSU, 35.6% had a consult order, of whom only one in three was reached for follow-up—or just more than 10% of all smokers admitted. Our analysis revealed a complicated mix of patient variables, medical team practices, and hospital work-flow issues that affected how many smokers could be reached with services and follow-up. When comparing the characteristics of smokers and nonsmokers, we were not surprised to find significant differences on most variables. The significant differences between subgroups of smokers with and without consult referrals were more unexpected. These differences could suggest that medical teams were more selective in their referrals than we had previously thought. TTS observations suggested that smokers admitted under urgent conditions seemed less able to participate in tobacco cessation treatment. To test this hypothesis, we included the six admission types in comparisons of patient characteristics and in our logistic regression models. Although we did not find that urgent versus planned admissions predicted outcomes, we did find that admission type itself predicted outcomes. This was an important finding because admission types identify distinctive hospital admission portals, each with implications for hospital work flow. This finding led us to recommend system changes to potentially improving consult service performance and outcomes (see Recommendations, page 216). The finding that the largest number of smokers were admitted for injuries predominantly through the ED and trauma unit was not surprising, given that OHSU is a Level 1 trauma center. What we didn’t necessarily expect was that smokers admitted through the ED would have the lowest rates of follow-up completion and abstinence. In a study of brief ED intervention, no differences in outcomes were found between treatment and control groups, suggesting that these treatments may not be as effective for smokers seen in the ED.25 Because 783 of the total number of 2,075 smokers referred for consults were admitted through the ED, 37.8% of all orders were for patients who were less likely to be reached for follow-up and less likely to quit. As indicated by the logistic regression analysis, admission for smoking-related illnesses, Medicaid or Medicare coverage, and hospital orders for medications to treat nicotine withdrawal were predictive, as was admission for elective medical procedures in predicting which variables were most significant for Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety predicting completion of the first step (consult order). The initial Joint Commission tobacco measures required provision of treatment for smokers with cardiac and respiratory illnesses,* and these OHSU medical teams continue to consistently order smoking cessation treatment for their patients. A history of mental disorders was also predictive for completing a consult order, which is not surprising, given that more than two thirds of smokers admitted had a history of mental disorders. It is not clear why there were more referrals for patients covered by Medicaid or Medicare than patients with commercial coverage. It is possible that patients with commercial coverage had other resources to help them quit, so were less interested. At the second step (consult completed) only medication orders was predictive. Medication and consult orders were done at the same time, usually for patients with more significant symptoms or who requested services, or both. These were also patients generally more receptive to consult services. Greater differences appear at the third step (follow-up arranged); smokers least likely to arrange a follow-up were those with the shortest consult times and admitted with injuries. Smokers with short consult times were typically those only willing to discuss withdrawal symptoms but unwilling to discuss quitting. The majority of injured smokers seen for a consult were admitted through the ED, with only a small number admitted to the trauma unit. Smokers admitted to the trauma unit were more likely to have follow-up arranged, while injured smokers admitted through the ED were not. We are not sure why these injured ED smokers were less likely to arrange a follow-up contact. We know that some of those patients had diagnoses of mental disorders, particularly addictions, which may have led to their injuries and subsequent discharge to treatment and rehabilitation facilities, limiting their availability for follow-up. At the fourth step, only smokers admitted for elective surgeries were significantly more likely to complete a follow-up call—and were most likely to complete all the treatment steps. Because smokers completing all steps reported a 47% abstinence rate at follow-up, we were most interested in the characteristics of these smokers. When compared to all other admission types, particularly smokers admitted through the ED, smokers admitted for elective surgery were much less likely to have a history of mental disorders (52% versus 79%), a primary diagnosis of mental disorders (0.1% versus 22%) or injuries (11% versus * These measures—PN-4 Adult Smoking Cessation Advice/Counseling, HF-4 Adult Smoking Cessation Advice/Counseling, and AMI-4 Adult Smoking Cessation Advice/Counseling measures were retired effective with January 1, 2012, discharges, after the tobacco treatment measure set was introduced, because the latter apply to all patients not just subsets of patients.
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19%) and to be uninsured (9% versus 22%) than those admitted through the ED. Although medical team referral rates were about the same for both these admission types, the number of smokers referred from the ED was about 40% greater than for elective surgery. Meanwhile, close to 70% of smokers admitted for elective surgeries did not receive tobacco cessation consult orders, although their recovery might benefit from abstinence.26–28 OHSU surgeons follow best practices and recommend that their patients quit tobacco use before surgery; some will not proceed unless the patient is completely abstinent. Even so, anecdotal information suggested that OHSU surgeons were reluctant to order nicotine replacement during hospitalization because of concern that nicotine would reduce blood flow and impede recovery. With only a third of elective surgery patients receiving a medication order compared to about half for other patients, it is possible that when nicotine replacement was not ordered before surgery, neither was a bedside consultation.
Recommendations Our effort to translate the results of research studies into everyday practice met with the complicated realities of applying evidence-based protocols in an uncontrolled setting. We found that applying a standard protocol across all patient populations resulted in highly variable outcomes, with some patients more likely to be able to complete treatment than others. The proportion of total smokers admitted compared to those actually able to be followed decreased substantially, limiting the effectiveness of our consult service. Our aim was to improve referral rates and outcomes. We found identifying optimal individual patient selection criteria for referrals difficult because patient circumstances are highly variable. However, we did identify three recommendations for improvement of the referral process that could help reach those smokers more likely to benefit. 1. Limit Inappropriate Referrals. For many smokers, hospitalization may not be the best time to initiate treatment. Their medical condition and life circumstances may be too unstable to participate in treatment and follow-up. Although all hospitalized smokers deserve the opportunity to receive help to quit, the evidence base is clear that specialized services are effective only if patients receive all treatment steps, including follow-up.2 Additional education needs to be developed to help medical teams quickly assess which patients are appropriate for referrals to specialized services on the basis of available resources and which patient are not. Patients appropriate for referral are those who are willing and medically stable enough to participate in the consultation while hospitalized and also able to complete follow-up. Volume 42 Number 5
Copyright 2016 The Joint Commission
The Joint Commission Journal on Quality and Patient Safety Although all patients should receive quitting information, some patients—such as homeless patients; patients who will be discharged to prison, rehabilitation, or nursing homes; or patients without a phone—might not be able to complete follow-up. 2. Increase Referrals of Motivated Smokers. Smokers already expressing interest in quitting are routinely referred regardless of other characteristics. However, medical teams often lack good information and tools to better assess patients’ willingness to participate, opting instead to refer patients only on the basis of their medical condition. With better assessment tools, referrals for additional motivated smokers could be increased. 3. Systematically Target Groups of Smokers Who Are Most Likely to Complete Follow-Up. In our analysis, these were smokers with elective surgical admissions. Because these surgeries are scheduled, patients can be systematically identified and their surgical teams prompted to refer more patients. Further, because many of those patients are first seen in the presurgery clinic, tobacco cessation treatment can be introduced before hospitalization,27 with follow-up during hospital stays. Although optimal timing for smoking cessation before surgery is uncertain, adding this treatment step could have the effect of improving cessation rates as well as surgical outcomes.
Strengths and Limitations This study is one of the first studies to report on the longterm experience of a hospital-based tobacco cessation service. Most published reports are based on the results of research studies. A strength of this analysis is that the OHSU population of smokers is large and data were collected for more than two years. This provides a comprehensive picture of implementation over time and a more complete description of the patient population and referral patterns that affect outcomes at OHSU. Even so, as a regional academic medical center, the population of OHSU smokers and referral patterns may differ from those at other hospitals. EHR data on race was incomplete and not included in this data set, limiting generalizability to other hospital settings. We also do not have reportable data on readiness to quit for all smokers, limiting our ability to identify motivation among patient group and link to outcomes. Because the only patients seen were those referred for services, the OHSU data reflect these referral decisions. Follow-up was limited to two to three weeks postdischarge because of limited resources. With a longer duration, different patterns of follow-up contact could emerge. Even so, our analysis has yielded new insights into addressing QI for our hospital-based services and could serve as an example for other hospitals seeking to improve their tobacco cessation services. May 2016
Conclusion Studies of tobacco cessation services for hospitalized patients have shown the potential for improving patient health outcomes. This study adds important information about the effects of real-world patient characteristics, medical team referrals, and the ability of patients to complete all steps of evidence-based treatment. Although developing the capacity to offer all hospitalized patients treatment for tobacco dependence may be optimal, typically only a small percentage of smokers are able to be reached. By adopting a more strategic approach to patient referrals, particularly with limited resources, the number of smokers reached could be increased, leading to improvements in both treatment quality and treatment outcomes. J This work was performed at Oregon Health & Science University Hospitals, Portland, Oregon, and supported by OHSU hospitals and the OHSU Division of Pulmonary and Critical Care Medicine. Wendy Bjornson reports unrestricted educational grants from Pfizer and ownership of 5 shares of Pfizer stock. David Gonzales reports research grants from Pfizer and Nabi Biopharmaceuticals; honoraria from GlaxoSmithKline and Gilead Sciences; and ownership of five shares of Pfizer stock.
Wendy G. Bjornson, MPH, and David H. Gonzales, PhD, are Co– Directors, Oregon Health & Science University (OHSU) Smoking Cessation Center, Division of Pulmonary and Critical Care Medicine, School of Medicine, OHSU, Portland, Oregon. Catherine J. Markin, MD, formerly Medical Director, OHSU Smoking Cessation Center, is Medical Director, Pulmonary and Sleep Services, Legacy Medical Group, Legacy Health, Portland, Oregon. Noal Clemons, BA, formerly Senior Research Assistant, OHSU Smoking Cessation Center, is Senior Research Assistant, Oregon Institute of Occupational Health Sciences, OHSU. Frances Favela, MPH, was Research Associate, OHSU Smoking Cessation Center. Trisha M. Coleman, MA, was Research Associate, OHSU Smoking Cessation Center. Caroline Koudelka, MPH, is Research Associate, and Jodi A. Lapidus, PhD, is Professor, Biostatistics & Design Program, OHSU-Portland State University (PSU) School of Public Health. Please address correspondence to Wendy Bjornson, [email protected].
Online Only Content http://www.ingentaconnect.com/content/jcaho/jcjqs See the online version of this article for Appendix 1. Summary of Odds Ratios for Significant Variables included in Analysis at Each Step of Consult Completion Appendix 2. Significant Differences in Proportions or Means of Demographic Variables for Elective Surgical Admission Type Compared to Other Admission Types
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The Joint Commission Journal on Quality and Patient Safety References
1. McBride CM, Emmons KM, Lipkus IM. Understanding the potential of teachable moments: The case of smoking cessation. Health Educ Res. 2003;18:156–170. 2. Rigotti NA, Munafo MR, Stead LF. Interventions for smoking cessation in hospitalised patients. Cochrane Database Syst Rev. 2007 Jul 18;3:CD001837. 3. Mohiuddin SM, et al. Intensive smoking cessation intervention reduces mortality in high–risk smokers with cardiovascular disease. Chest. 2007;131: 446–452. 4. Quist-Paulsen P, Gallefoss F. Randomised controlled trial of smoking cessation intervention after admission for coronary heart disease. BMJ 2003 Nov 29;327:1254–1257. 5. Colivicchi F, et al. Effect of smoking relapse outcome after coronary syndromes. Am J Cardiol. 2011 Sep 15;108:804–808. 6. Smith PM, Burgess E. Smoking cessation initiated during hospital stay for patients with coronary artery disease: A randomized controlled trial. CMAJ. 2009 Jun 23;180:1297–1303. 7. Rigotti NA. Integrating comprehensive tobacco treatment into the evolving US healthcare system: It’s time to act. Arch Intern Med. 2011 Jan 10;171:53–55. 8. The Joint Commission. Tobacco Treatment (TOB) National Hospital Inpatient Quality Measures. In Specifications Manual for National Hospital Inpatient Quality Measures, version 5.0b: Discharges 10-01-15 (4Q15) Through 06-30-16 (2Q16). Oak Brook, IL: Joint Commission Resources, 2015. Accessed Mar 23, 2016. http://www.jointcommission.org/assets/1/6/NHQM_v5_0_b_PDF_11 _13_2015.zip. 9. Fiore MC, et al. Treating Tobacco Use and Dependence: 2008 Update. Clinical Practice Guideline. Rockville, MD: US Department of Health and Human Services, Public Health Service, 2008. 10. HealthIT.gov. EHR Incentives & Certification: EHR Incentive Programs: Medicare and Medicaid EHR Incentive Programs. (Updated: Jan 15, 2013.) Accessed Mar 24, 2016. https://www.healthit.gov/providers-professionals /ehr-incentive-programs. 11. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010 Aug 5;363:501–504. 12. Partnership for Prevention. Helping Patients Quit: Implementing the Joint Commission Tobacco Measure Set in Your Hospital. Accessed Mar 24, 2016. https://www.prevent.org/downloadStart.aspx?id=54. 13. Smoking Cessation Leadership Center. Destination Tobacco-Free: A Prac-
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tical Tool for Hospitals and Health Systems. 2013. Accessed Mar 24, 2016. http://smokingcessationleadership.ucsf.edu/resources/toolkits. 14. Smith PM, Taylor CB. Implementing an Inpatient Smoking Cessation Program. Mahwah, NJ: Lawrence Erlbaum Associates, 2006. 15. Duffy SA, et al. Implementation of the Tobacco Tactics program in the Department of Veterans Affairs. J Gen Intern Med. 2010;25 Suppl 1:3–10. 16. Partnership for Prevention. Working with Healthcare Delivery Systems to Improve the Delivery of Tobacco-Use Treatment to Patients. Apr 2008 (Updated: Apr 2009.) Accessed Mar 24, 2016. http://www.prevent.org/download Start.aspx?id=23. 17. Gadomski AM, et al. Effectiveness of an inpatient smoking cessation program. J Hosp Med. 2011;6:E1–8. 18. Faseru B, et al. Evaluation of a hospital–based tobacco treatment service: Outcomes and lessons learned. J Hosp Med. 2011;6:211–218. 19. Fung PR, et al. Effectiveness of hospital-based smoking cessation. Chest. 2005;128:216–223. 20. Oregon Health Authority. Quit for Life® Program. About the Program. Accessed Mar 24, 2016. https://www.quitnow.net/oregon/About/. 21. Smith PM, et al. In–hospital smoking cessation programs: Who responds, who doesn’t? J Consult Clin Psychol. 1999:67:19–27. 22. Faseru B, et al. Prevalence and predictors of tobacco treatment in an academic medical center. Jt Comm J Qual Patient Saf. 2009;35:551–557. 23. Benjamini, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995;57:289–330. 24. Richter KP, Ellerbeck EF. It’s time to change the default for tobacco treatment. Addiction. 2015;110:381–386. 25. Katz DA, et al. The emergency department action in smoking cessation (EDASC) trial: Impact on cessation outcomes. Nicotine Tob Res. 2013;15: 1032–1043. 26. Cropley M, et al. The effectiveness of smoking cessation interventions prior to surgery: A systematic review. Nicotine Tob Res. 2008;10:407–412. 27. Møller AM, et al. Effect of preoperative smoking intervention on postoperative complications: A randomised clinical trial. Lancet. 2002 Jan 12:359: 114–117. 28. Thomsen T, Villebro N, Møller AM. Interventions for preoperative smoking cessation. Cochrane Database Syst Rev. 2014 Mar 27;3:CD002294.
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Appendix 1. Summary of Odds Ratios for Significant Variables Included in Analysis at Each Step of Consult Completion 1. Consult Referral OR (95% CI)
OR (95% CI)
3. Follow-Up Arranged OR (95% CI)
p Value*
p Value*
4. Follow-Up Completion OR (95% CI)
p Value*
1.06 (0.94–1.20)†
0.3219
1.36 (1.07–1.74)
0.0575
1.34 (1.07–1.67)†
0.0171
1.20 (0.88–1.63)†
0.2522
1.00 (1.00–1.01)†
0.8367
1.01 (1.00–1.02)
0.2113
1.00 (1.00–1.01)†
0.3424
1.01 (1.00–1.02)
0.1270
2.41 (1.64–3.55) 0.83 (0.69–0.99) 0.69 (0.51–0.94) 1.09 (0.93–1.28) 0.98 (0.75–1.26)
< 0.0001
1.52 (0.79–2.94) 1.52 (1.07–2.17) 0.91 (0.47–1.73) 1.48 (1.09–2.01) 1.07 (0.67–1.69)
0.3068
2.44 (1.34–4.45) 1.56 (1.13–2.16) 2.54 (1.30–4.96) 1.50 (1.14–1.99) 1.78 (1.13–2.79)
0.0117
2.28 (1.04–5.01) 2.41 (1.55–3.77) 0.77 (0.35–1.70) 0.95 (0.66–1.38) 0.89 (0.50–1.58)
0.0975
Cessation Medication Order Yes vs. No
NA
NA
2.04 (1.62–2.57)
< 0.0001
1.58 (1.26–1.99)
0.0006
0.88 (0.59–1.32)†
0.5443
History of Mental Disorders Yes vs. No
1.53 (1.34–1.75)
< 0.0001
1.30 (1.01–1.68)
0.1270
0.94 (0.73–1.22)†
0.6523
1.37 (0.96–1.95)
0.1270
Insurance Status Medicaid vs. Commercial/Contract Medicare vs. Commercial/Contract Nonsponsored vs. Commercial/Contract Other sponsored vs. Commercial/Contract
1.24 (1.04–1.47) 1.32 (1.11–1.56) 1.22 (1.00–1.49) 1.26 (0.95–1.67)
0.0268
0.77 (0.54–1.09)† 0.76 (0.52–1.10)† 0.87 (0.58–1.30)† 0.73 (0.42–1.27)†
0.1395†
1.30 (0.94–1.79)† 1.16 (0.83–1.62)† 1.33 (0.92–1.94)† 0.99 (0.57–1.72)†
0.1127†
0.81 (0.51–1.29)† 0.81 (0.50–1.32)† 0.92 (0.54–1.57)† 0.40 (0.19–0.86)†
0.3748†
Sex
Male vs. Female
p Value*
2. Consult Completion
Patient Age (per year increase) Admission Type Elective Medical vs. Emergent Elective Surgical vs. Emergent Trauma vs. Emergent Urgent Medical vs. Emergent Urgent Surgical vs. Emergent
0.0572 0.0344 0.3618 0.8562
0.0037 0.0724 0.1353
0.0102 0.8272 0.0575 0.7807
0.1420† 0.4968† 0.2645†
0.0152 0.0152 0.0117 0.0202
0.3808† 0.1344† 0.9811†
0.0013 0.6831 0.8167 0.8167
0.3914† 0.7546† 0.0195†
(continued on page AP2)
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Appendix 1. Summary of Odds Ratios for Significant Variables Included in Analysis at Each Step of Consult Completion (continued) 1. Consult Referral OR (95% CI) Primary Discharge Diagnosis Yes vs. No Cancer Endocrine Cardiovascular Mental disorders Pulmonary Gastrointestinal Genitourinary Orthopedic Injury Other Prescription at Discharge Yes vs. No
1.27 (1.03–1.56) 1.78 (1.28–2.47) 3.52 (2.99–4.15) 0.10 (0.07–0.14) 3.10 (2.33–4.12) 1.37 (1.12–1.67) 1.27 (0.92–1.74) 0.90 (0.71–1.14) 0.73 (0.61–0.86) 0.97 (0.84–1.13)
p Value*
0.0457 0.0015 < 0.0001 < 0.0001 < 0.0001 0.0040 0.1764 0.4264 0.0008 0.7703
2. Consult Completion OR (95% CI)
p Value*
1.1 (0.70–1.73 1.72 90.84–3.52) 1.52 (1.21–2.07) 0.59 (0.30–1.18) 1.16 (0.72–1.88) 0.80 (0.55–1.15) 0.67 (0.37–1.21) 0.73 (0.45–1.17) 1.01 (0.71–1.45 0.71 (0.53–0.94)
0.8140 0.2640 0.0575 0.2640 0.6771 0.3068 0.3068 0.3068 0.9481 0.0630
3. Follow-Up Arranged OR (95% CI)
p Value*
OR (95% CI)
p Value*
1.18 (0.80–1.74) 1.47 (0.85–2.54) 1.57 (1.21–2.03) 0.64 (0.29–1.40) 0.87 (0.57–1.32) 1.40 (0.96–2.05) 1.13 (0.62–2.08) 0.63 (0.39–1.02) 0.59 (042–0.83) 0.76 (0.57–1.00)
0.4389
0.94 (0.54–1.65)†
0.8418†
0.2103
0.80 (0.40–1.62)† 1.35 (0.93–1.98)†
0.5354†
0.51 (0.16–1.64)† 1.15 (0.60–2.22)† 0.81 (0.49–1.34)† 0.73 (0.31–1.69)† 0.79 (0.35–1.78)† 1.10 (0.64–0.91)† 0.92 (0.60–1.39)†
0.2562†
0.0029 0.3124 0.5352 0.1047 0.6848 0.0836 0.0100 0.0709
NA
NA
NA
NA
NA
NA
Contact Time (minutes): 1–5 vs. 6–10
NA
NA
NA
NA
< 0.0001
11–15 vs. 6–10
NA
NA
NA
NA
> 15 vs. 6–10
NA
NA
NA
NA
0.23 (0.17–0.31 ) 1.42 (1.09–1.84) 2.39 (1.62–3.53)
1.03 (1.02–1.04)
< 0.0001
1.00 (0.99–1.01)†
0.7707
1.01 (0.99–1.03)†
0.3733
Length of Stay (per-day increase)
4. Follow-Up Completion
0.0171 0.0001
0.74 (0.54–1.01)
0.1194†
0.6722† 0.4090† 0.4635† 0.5757† 0.7295† 0.6748†
0.1136
0.94 (0.54–1.62) 1.46 (1.02–2.08) 1.81 (1.13–2.91)
0.8167
0.98 (0.95–1.00)
0.0975
0.0975 0.0830
OR, odds ratio; CI, confidence interval; NA, use of the variable was not appropriate or not available for the indicated stage of analysis. * p values for odds ratios of individual comparisons are adjusted for multiple comparisons by false discovery rate (FDR) method. † Variables not found to be predictive (p > 0.1000) were removed from final models. Unadjusted model p values and odds ratios of these variables are reported.
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Appendix 2. Significant Differences in Proportions or Means of Demographic Variables for Elective Surgical Admission Type Compared to Other Admission Types* Urgent Medical
Elective Surgical
Elective Medical
Emergent
Trauma
Urgent Surgical
n
%
p Value†
n
%
n
%
n
%
n
%
n
%
SexFemale Male
707 687
50.7% 49.3%
< 0.0001
667 561
54.3 45.7
81 53
60.4 39.6
832 1,476
36.0 64.0
76 268
22.1 77.9
141 184
43.4 56.6
Insurance Class Commercial/Contracts Medicaid Medicare Nonsponsored
464 333 412 126
33.3 23.9 29.6 9.0
< 0.0001
18.1 39.7 24.8 11.9
46 39 45 3
34.3 29.1 33.6 2.2
350 669 630 497
15.2 29.0 27.3 21.5
150 52 33 64
24.3 24.6 29.8 16.0
5.5
1
0.7
162
7.0
45
43.6 15.1 9.6 18.6 13.1
79 80 97 52
Other sponsored
222 487 305 146 68
17
5.2
59
4.2
History of Mental Disorders
723
52.1
< 0.0001
878
72.3
96
71.6
1,803
78.1
209
60.8
225
69.2
Cessation Medication Order
469
33.6
< 0.0001
571
46.5
69
51.5
1,225
53.1
151
43.9
138
42.5
Primary Discharge Diagnosis‡ Cancer Endocrine Cardiovascular Mental disorders Pulmonary Gastrointestinal Genitourinary Orthopedics Injury Other
398 28 126 1 17 105 60 348 154 150
28.7 2.0 9.1 0.1 1.2 7.6 4.3 25.1 11.1 10.8
< 0.0001
59 28 327 34 39 100 24 21 129 453
4.9 2.3 26.9 2.8 3.2 8.2 2.0 1.7 10.6 37.3
18 1 31 10 0 4 0 0 0 70
13.4 0.7 23.1 7.5 0.0 3.0 0.0 0.0 0.0 52.2
46 91 236 509 157 228 91 64 432 443
2.0 4.0 10.3 22.2 6.8 9.9 4.0 2.8 18.8 19.3
2 1 3 11 1 0 1 1 319 5
0.6 0.3 0.9 3.2 0.3 0.0 0.3 0.3 92.7 1.5
21 5 100 2 6 38 3 11 77 59
6.5 1.6 31.1 0.6 1.9 11.8 0.9 3.4 23.9 18.3
Prescription at Discharge
135
9.7
0.2946
161
13.1
34
25.4
206
8.9
20
5.8
42
12.9
63 172 91 45
17.0 46.4 24.5 12.1
0.0773
94 223 107 45
20.0 47.5 22.8 9.6
11 24 22 9
16.7 36.4 33.3 13.6
159 274 127 54
25.9 44.6 20.7 8.8
14 26 12 3
25.5 47.3 21.8 5.5
19 54 30 13
16.4 46.6 25.9 11.2
Mean
SD
p Value§
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Consult Time (minutes) 1–5 6–10 11–15 > 15
Age in Years
51.6
14.2
< 0.0001
46.2
16.6
46.9
15.1
45.9
14.9
41.2
15.7
51.4
15.4
Length of Stay
4.1
4.5
< 0.0001
5.6
5.9
5.2
5.6
5.2
7.0
5.8
10.2
8.5
16.3
SD, standard deviation. * Discrepancies for mental disorders compared to values in Table 1 due to missing values; total denominator = 3,934, that is, with the subtraction of the 40 missing values (3 from admission type and 37 from the “other” category). Resulting denominators for the admission type are 1,389 (5) for elective surgical, 1,215 (13) for urgent medical, 2,297 (11) for emergent, and 323 (2) for urgent surgical. †
Chi-square p value compares proportions of admission type subgroup to all other subgroups combined.
‡
Discrepancies in column totals for primary discharge diagnosis compared to Table 1 are due to missing values. The resulting denominators are 1,387 (missing 7) for elective surgical,1,214 (14) for urgent medical, 2,297 (11) for emergent, and 322 (3) for urgent surgical.
§
p values compare mean/median of admission type subgroup to all other subgroups combined by t-test (age) and Wilcoxon rank-sum test (length of stay).
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Copyright 2016 The Joint Commission
AP3
Two Years in the Life of a University Hospital Tobacco Cessation Service: Recommendations for Improving the Quality of Referrals Wendy G. Bjornson, MPH; David H. Gonzales, PhD; Catherine J. Markin, MD; Noal Clemons, BA; Frances Favela, MPH; Trisha M. Coleman, MA; Caroline Koudelka, MPH; Jodi A. Lapidus, PhD
H
ospitalization provides an opportunity to offer tobacco cessation services for patients who otherwise might not seek help; because of illness, these patients may be more receptive to quitting.1 The effectiveness of initiating evidence-based treatment during this “teachable moment,” particularly for cardiac patients, has been reported in previous studies.2–6 Tobacco cessation services during hospitalization as a potential strategy for improving health outcomes and reducing costs is gaining momentum.7 The Joint Commission now includes four inpatient tobacco treatment core measures.8 The US Public Health Service Guideline, Treating Tobacco Use and Dependence: 2008 Update (PHS Guideline), includes recommendations for hospital patients and administrators.9 Finally, the Centers for Medicaid & Medicare Services (CMS) has established an incentive program for phasing in “meaningful use” of electronic health records (EHRs), with documentation for treatment of tobacco use among the core objectives.10,11 As more hospitals in the United States implement tobacco treatment services for all patients and incorporate them into EHR systems, additional guidance will be needed. Design and implementation guidelines for hospital services are described in detail elsewhere.12–16 These services generally follow the PHS Guideline as translated to hospital systems. The Joint Commission measures are designed to evaluate implementation of these guidelines in hospitals and provide a basis for quality improvement (QI). These measures evaluate the provision of (1) tobacco use screening for all patients, (2) tobacco use treatment (counseling and medications) during the hospital stay, (3) tobacco use treatment offered to sustain abstinence after discharge, and (4) follow-up coaching/counseling after discharge.8* Hospital-based tobacco cessation services have been shown to be effective only when all treatment steps are completed.2 * TOB-4, Tobacco Use: Assessing Status After Discharge, requires a follow-up call to the patient during the 30 days after discharge to assess tobacco use status, adherence to cessation medication, and continued participation in counseling. TOB-4 has been temporarily suspended since January 1, 2015, because of feasibility of data collection.
May 2016
Article-at-a-Glance Background: Hospitalization, when patients may be more
receptive to quitting, provides an opportunity to provide tobacco cessation services for patients who otherwise might not seek help. Although specialized tobacco cessation services are shown to be effective if evidence-based treatment, including follow-up, is completed, resources are limited and guidelines are needed, and few smokers complete all treatment steps. Experience drawn from an analysis of two-year implementation data from the Oregon Health & Science University (OHSU) Tobacco Cessation Consult Service is presented. Methods: Data for 5,827 smokers discharged from OHSU University hospital between January 2011 and December 2012 were analyzed to determine patient characteristics and identify predictors of completing each of four treatment steps: consult ordered, consult completed, follow-up arranged, and follow-up completed. Results: Smokers were younger and male (p < 0.0001) and significantly different with respect to insurance class, admission type, history of mental disorders, primary discharge diagnoses, and length of stay (p < 0.0001) than nonsmokers. Predictors of having a tobacco consult order were admission for elective medical procedures; orders for medications to treat withdrawal; history of mental health/substance use disorders; primary diagnoses of cardiovascular, endocrine, gastrointestinal, or pulmonary disease; and longer hospitalizations. Smokers admitted through the emergency department had the lowest rates of follow-up completion and abstinence. Admission for an elective surgery was the only predictor of completing all treatment steps through followup (p ≤ 0.05). Conclusions: This study adds important information about how hospitalized smokers respond to each step of tobacco treatment in a real-world setting and offers strategies for improving referrals.
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The Joint Commission Journal on Quality and Patient Safety There are limited published data on implementation of these services across all hospital patients and on what proportion of patients completed all treatment steps, including follow-up. Some published studies report results only for specific patient (such as cardiac) populations.3–6 We are aware of only two published studies that report on implementation for all patients.17,18 In one of these studies, 58.3% of the treatment group and 43.0% of the nontreatment group completed all treatment steps and atients were contacted for follow-up.17 In the other study, of all p identified as tobacco users, 18.1% were referred for cessation services. In-hospital treatment was completed for 68.1% of those referred, and 38.2% of treated patients were reached for follow-up (about 5% of the total population of smokers).18 Some of the reasons for failure to complete follow-up were deceased; resided at a nursing home; refused; too sick to compete interview; and, most frequently, lost to follow-up. In both of these studies, staffing was limited to ≤ 1.0 full-time equivalent (FTE), which limited patient reach. Unlike those two studies, which did not use exclusion criteria for selecting patients and instead offered services to “all comers,” another study’s exclusion criteria, which include a history of drug or alcohol addiction, cancer, depression or psychiatric illness, and inability to attend follow-up, eliminated more than 70% of initially eligible patients.19 On the basis of limited published data, then, an all-comers selection strategy results in significant attrition at each step, with few patients receiving complete evidence-based treatment. Yet application of strict exclusion criteria, unsurprisingly, eliminates many patients. Further, when hospital budgets limit funding there is insufficient staff to reach the many patients who may need services. A more strategic approach is needed to improve the effectiveness of program services despite limited staff and help improve outcomes. We are not aware of any published reports that address how outcomes of hospital-based tobacco cessation services might be improved. In this article, we describe and report results for a tobacco cessation consult service at a large academic medical center. We had observed that some of our medical teams were not referring many patients, while others were referring many patients who could not complete treatment. We wanted to develop strategies to improve the quality of medical team referrals and the overall effectiveness of our services within the constraints of our limited resources. Our purpose for the analysis was twofold: (1) to compare smokers versus nonsmokers and smokers with and without consult orders admitted to our hospital and discharged between January 2011 and December
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2012, and (2) to identify predictors for moving through each step of tobacco cessation services provided for our patients.
Methods Setting Oregon Health & Science University (OHSU), located in Portland, Oregon, is a 543-bed, Level 1 trauma and teaching hospital that admits nearly 23,000 adult patients annually. OHSU implemented a Tobacco Cessation Consult Service (Consult Service) for hospital patients in 2007 in conjunction with a 100% tobacco-free campus policy. The Consult Service was implemented (including staff training) across all units and was available to any patient who reported smoking at admission. The Consult Service is managed through the OHSU Smoking Cessation Center (SCC).
Procedures Patients were referred to the Consult Service by physicians and nurses through the EHR. Smoking status was assessed at admission, orders for stop-smoking medications were entered into the EHR, and orders for a bedside consult were placed on the Consult Service patient list. A master’s-level-trained tobacco treatment specialist (TTS) first reviewed the list and patients’ medical records, initiated consult documentation, and then attempted at least three times within 48 hours to see each patient for a bedside consult.
Tobacco Cessation Consult Protocol The consult followed a common protocol, which consisted of an assessment of nicotine withdrawal symptoms, current medication orders, present use of any tobacco cessation medications, and ongoing use and dose of medications on the basis of withdrawal symptoms. The TTS completed a tobacco use history/assessment, determined willingness of the patient to remain abstinent after discharge, discussed a discharge plan including cessation medications, and asked permission to follow up by phone two weeks postdischarge. A consult was considered complete if the TTS was able to evaluate withdrawal, determine willingness to discuss smoking, and provide tobacco cessation information. The TTS then entered notes into the EHR and contacted the medical team with an update. For patients unwilling or unable to have a discussion, the TTS arranged to come back another time, leaving written information for later use. All patients seen by the TTS received OHSU–tailored patient information about quitting tobacco use, including con regon Tobacco Quit Line, and were tact information for the O 20 encouraged to call.
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Copyright 2016 The Joint Commission
The Joint Commission Journal on Quality and Patient Safety After a consult was completed, the TTS entered information into the SCC administrative database. Patients giving permission for follow-up were flagged, and up to three contact attempts were made, beginning two weeks after discharge. When contacted, the TTS determined smoking status and use of programs and medications, answered questions, provided encouragement, made additional recommendations, and entered the follow-up data. For patients requesting more help, an additional contact was offered. Limited resources prevented additional follow-up for all patients.
Research Questions Our analysis was designed to address the following questions: 1. Are there differences between characteristics of OHSU smokers versus nonsmokers and smokers with and without consult referrals? Do these differences help explain outcomes? 2. How do smokers move through the cessation treatment steps offered at OHSU? 3. What characteristics predict completing each treatment step? 4. What characteristics predict completing all treatment steps? 5. What QI changes are suggested?
Database Data stored in the SCC database were combined with selected and Institutional Review Board–approved data from the OHSU Research Data Warehouse (RDW). The variables for all patients were as follows: age, sex, type of admission (emergent, trauma, urgent medical, elective medical, urgent surgical, elective surgical), type of insurance coverage (or no coverage), any history of mental disorders (including addictions), length of stay, length of consult, and the primary and up to three additional discharge diagnoses (International Statistical Classification of Diseases and Related Health Problems, Ninth Revision [ICD–9] codes). Variables for smokers included smoking cessation medication orders while hospitalized. For smokers seen for a consult, variables included the length of the consult and, if reached by phone, self-reported smoking status (smoking more, less, or abstinent since discharge) at the two-week follow-up call. These variables were chosen because they were predictive in other studies (for example, history of mental disorders,9,21 discharge diagnosis2,18,22) or because clinical observations suggested they (for example, type of admission) might be predictive for the OHSU population.
Statistical Methods Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina). Significance tests for May 2016
comparisons of current smokers versus current nonsmokers reported in Table 1 (page 212) were performed by t-test for the continuous variable age (normal distribution), Wilcoxon ranksum test for the continuous variable length of stay (skewed distribution), and by chi-square test for binary variables sex, history of mental disorders, and orders for cessation medications. For categorical variables with more than two groups (insurance class, admission type, and primary discharge diagnosis), chi-square tests were performed on each variable level versus all other combined, and p values were adjusted for multiple comparisons by false discovery rate method.23 Chi-square tests (categorical variables) and t-tests (continuous variables) were used to determine p values to compare proportions and means of demographic variables between elective surgical patients and all other patient types combined, as reported in Appendix 2.
Logistic Regression Analysis Odds ratios (ORs) for predictors of subject participation or inclusion at each measurement step (consult referral, consult completed, follow-up arranged, follow-up completed [reported in Appendix 1]) were determined using multivariate logistic regression models generated for each step. Regression models for all steps used independent variables gender, age, insurance type, history of mental disorders, time spent at patient contact, length of hospital stay, smoking cessation medications given in hospital, smoking cessation medications prescribed at discharge, and any of various diagnoses (cardiovascular disease, pulmonary disease, cancer, endocrine disorder, injury, orthopedic, gastrointestinal, and genitourinary) to predict subject inclusion. Three independent variables were used selectively in the following measurement step models: (1) “medication orders” was removed from the consult referral model because they were typically done together, (2) “time spent” was included only for the “follow-up arranged” and “follow-up completed” models, and (3) “medications at discharge” was included only for the “follow-up completed” model. Models were developed via the backward elimination method, whereby variables not found to be significant predictors of subject inclusion (p > 0.10) were removed from the model.
Results Patient Characteristics Table 1 compares characteristics of current smokers versus current nonsmokers/former smokers and smokers with and without consult referrals to provide an overview of the smoking population and the subgroup referred for treatment. Compared Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety Table 1. Population Comparisons: Non/Former Smokers Versus Smokers and Smokers With and Without Consult Orders* Non/Former Smokers No. (%) 22,438 Average Age (at admission) Gender Male
79.4
Smokers No. (%) 5,827
52
p Value†
20.6 47
Smokers Without Consult Orders No. (%) 3,752
< 0.0001
2,075
46
p Value†
35.6 49
< 0.0001
9,271
41.3
3,283
56.3
13,167
58.7
2,544
43.7
Commercial/Contracts
8,955
39.9
1,323
22.7
< 0.0001
928
24.7
395
19.1
Medicaid
3,590
16.0
1,675
28.8
< 0.0001
1,059
28.2
616
29.7
0.2807
Medicare
7,836
34.9
1,536
26.4
< 0.0001
899
24.0
637
30.7
< 0.0001
Nonsponsored
1,119
5.0
904
15.5
< 0.0001
588
15.7
316
15.3
0.6712
Other/unknown sponsored
938
4.2
386
6.6
< 0.0001
278
7.4
108
5.2
0.0021
5,092
22.7
2,308
39.6
< 0.0001
1,525
40.6
783
37.8
0.0329
603
2.7
344
5.9
< 0.0001
272
7.3
72
3.5
< 0.0001
6,271
28.0
1,228
21.1
< 0.0001
676
18.0
552
26.6
< 0.0001
884
3.9
134
2.3
< 0.0001
56
1.5
78
3.8
< 0.0001 0.0013
Female
< 0.0001
64.4
Smokers With Consult Orders No. (%)
2,096
55.9
1,187
57.2
1,656
44.1
888
42.8
0.3229
Insurance Class < 0.0001
Admission Type Emergent Trauma Urgent medical Elective medical Urgent surgical
1,154
5.1
325
5.6
0.1818
182
4.9
143
6.9
Elective surgical
7,906
35.2
1,394
23.9
< 0.0001
950
25.3
444
21.4
0.0012
528
2.4
91
1.6
0.0003
91
2.4
0
0.0
< 0.0001
6,691
29.8
3,974
68.6
< 0.0001
2,445
65.2
1,529
74.8
< 0.0001
3.8
2,633
45.2
< 0.0001
1,331
35.5
1,302
62.8
< 0.0001
3,192
14.2
556
9.5
< 0.0001
360
9.6
196
9.4
0.8528
850
3.8
155
2.7
< 0.0001
82
2.2
73
3.5
0.0037
2,619
11.7
837
14.4
< 0.0001
323
8.6
514
24.8
< 0.0001
Mental disorders
485
2.2
570
9.8
< 0.0001
525
14.0
45
2.2
< 0.0001
Pulmonary
582
2.6
221
3.8
< 0.0001
86
2.3
135
6.5
< 0.0001
1,690
7.5
481
8.3
0.0649
291
7.8
190
9.2
0.0808 0.8104
Other History of Mental Disorders Yes
Cessation Medication Orders Yes
861
Primary Discharge Diagnosis Cancer Endocrine Cardiovascular
Gastrointestinal Genitourinary
836
3.7
179
3.1
0.0189
113
3.0
66
3.2
Orthopedics
2,179
9.7
445
7.6
< 0.0001
325
8.7
120
5.8
0.0001
Injury
2,917
13.0
1,117
19.2
< 0.0001
830
22.1
287
13.8
< 0.0001
Other
6,927
31.1
1,194
20.8
779
21.0
415
20.3
Length of Stay Mean (SD)
4.70 (5.71)
5.20 (7.39)
0.0029†
4.70 (6.24)
6.11 (9.01)
< 0.0001‡
SD, standard deviation. * Discrepancies in column totals for Insurance Class, Admission Type, History of Mental Disorders, and Primary Discharge Diagnosis are due to missing values. Insurance Class and Admission Type has 3 missing values for smokers and smokers with consult orders; History of Mental Disorders has 31 missing values for smokers and smokers with consult orders; Primary Discharge Diagnosis has 161 missing values for nonsmokers, 72 missing values for smokers, 38 missing values for smokers without consult orders, and 34 missing values for smokers with consult orders. † p values determined by t-test for continuous variable age and by chi-square test for binary variables sex, history of mental disorders, and orders for cessation medications. For categorical variables—insurance class, admission type, and primary discharge diagnosis—chi-square tests were performed on each variable level versus all others combined and adjusted for multiple comparisons by the false discovery rate method. ‡
Wilcoxon rank-sum test (nonparametric) was used for length of stay to compare skewed (nonnormal) distributions.
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The Joint Commission Journal on Quality and Patient Safety to nonsmokers/former smokers, current smokers were significantly more likely to be younger, male, Medicaid-eligible or uninsured, and admitted through the emergency department (ED) or trauma unit, and they were more than twice as likely to have a history of mental disorders (p < 0.0001). Current smokers were also significantly more likely to have a primary discharge diagnosis of cardiovascular disease, mental disorders, pulmonary disease, or injuries (p < 0.0001). Smokers with consult referrals were more likely to be older; male; covered by commercial insurance and Medicare; admitted for urgent and elective medical procedures and urgent surgical procedures; and have a history of mental disorders, endocrine, cardiovascular, and pulmonary discharge diagnoses, and longer hospitalizations.
Patient Flow From January 2011 through December 2012, 5,827 current smokers were discharged from the OHSU hospital, 2,075 (35.6%) of whom were referred for a tobacco cessation consult. Of those patients referred, 1,683 (81.1%) consults were completed. “Patient discharged early” was the primary reason for consults not completed. Among patients with completed consults, nearly half (47.2%) were not scheduled for a follow-up call. Typically, these patients had no phone or were being discharged to a facility without private-phone access (for example, prison, rehabilitation/care facilities). Of patients agreeing to be called, 613 (69.0%) were reached (36.4% of all patients with completed consults and 10.5% of all smokers). Of those reached, 47.0% reported abstinence (4.9% overall), 42.6% reported reduced smoking, and 10.4% reported relapse to smoking > levels before admission (Figure 1, right).
Attrition from Referral to Follow-Up Table 2 (page 214) shows the proportion of total smokers referred for a consult by selected variables and rates of follow-up completion at each step, with smokers remaining at the previous step as the denominator. The composition of patients at each step changes and can be determined by comparison of results within subgroups of variables. For example, Medicare patients had proportionately higher rates of referrals, while commercially insured patients had proportionately higher rates of follow-up completion.
Predictors of Completion of Treatment Steps Appendix 1 (available in online article) shows ORs, confidence intervals (CIs), and p values of treatment-step completion by patient characteristics; we now describe the results. May 2016
Patient Flow Total current smokers discharged Jan 2011–Dec 2012 N = 5,827
Current smokers with unique orders for tobacco consult services Jan 2011–Dec 2012 N = 2,075
Consults completed N = 1,683
Arrange follow-up call N = 888
Patient reached N = 613
Abstinent n = 288 (47.0%)
Reduced n = 261 (42.6%)
Current smokers without consult orders N = 3,752 (64.4%) Incomplete n = 392 (18.9%) • Discharged early n = 261 • Canceled orders n = 49 • Clinically unable to see n = 36 • Unavailable with 3+ attempts n = 18 • Patient refused n = 13 • Other n = 15 No follow-up call n = 795 (47.2%) • Not appropriate (e.g., d/c to prison, nursing home, rehab; no phone) n = 463 • Declined n = 321 • Forgot to offer n = 11 Not reached n = 275 (31.0%) • Not reached after 3+ attempts n = 174 • Not at given number n = 67 • In treatment facility n = 5 • Deaths n = 4 • Other n = 25 Relapsed ≥ previous use n = 64 (10.4%)
Figure 1. The patient flow, starting with a total of 5,827 current smokers discharged from January 2011 through December 2012, is shown. d/c, discharged.
Step 1. Consult Referral. Patients significantly more likely to be referred for a consult were those who were admitted for an elective medical procedure; had a history of mental disorders; were covered by Medicaid or Medicare; had a primary diagnosis of cardiovascular disease, endocrine disorder, gastrointestinal disorder, or pulmonary disease; and/or had incrementally longer hospital stays. Patients least likely to be referred for a consult were those admitted for trauma and those with a primary diagnosis of an injury or mental disorder. Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety Table 2. Number and Percentage of Smokers at Each Treatment Step by Subgroups (Smokers Included in Previous Step as Denominator)*
All
Total Smokers
1. Consult Referral
2. Consult Completion
3. Follow-Up Arranged
4. Follow-Up Completion
n
n (%)
n (%)
n (%)
n (%)
n (%)
2,075 (35.6)
1,683 (81.1)
888 (52.8)
613 (69.0)
288 (47.0)
5,827
Sex
Abstinent
Female
2,544
888 (34.9)
740 (83.3)
413 (55.8)
279 (67.6)
123 (44.1)
Male
3,283
1,187 (36.2)
943 (79.4)
475 (50.4)
334 (70.3)
165 (49.4)
Insurance Class† Commercial/Contracts
1,323
395 (29.9)
327 (82.8)
172 (52.6)
129 (75.0)
70 (54.3)
Medicaid
1,675
616 (36.8)
495 (80.4)
264 (53.3)
177 (67.0)
74 (41.8)
Medicare
1,536
637 (41.5)
522 (81.9)
278 (53.3)
195 (70.1)
91 (46.7)
Nonsponsored
904
316 (35.0)
254 (80.4)
135 (53.1)
92 (68.1)
40 (43.5)
Other
386
108 (28.0)
83 (76.9)
37 (44.6)
20 (54.1)
13 (65.0)
Emergent
2,308
783 (33.9)
609 (77.8)
266 (43.7)
175 (65.8)
58 (33.1)
Admission Type‡
Trauma
344
72 (20.9)
54 (75.0)
29 (53.7)
16 (55.2)
8 (50.0)
Urgent medical
1,228
552 (45.0)
466 (84.4)
263 (56.4)
169 (64.3)
98 (58.0)
Elective medical
134
78 (58.2)
66 (84.6)
46 (69.7)
37 (80.4)
12 (32.4)
Urgent surgical
325
143 (44.0)
114 (79.7)
68 (59.6)
43 (63.2)
25 (58.1)
Elective surgical
1,394
444 (31.9)
372 (83.8)
214 (57.5)
173 (80.8)
87 (50.3)
History of Mental Disorders
3,974
1,529 (38.5)
1,257 (82.2)
661 (52.6)
462 (69.9)
207 (44.8)
Cessation Medication Orders
2,633
1,302 (49.4)
1,105 (84.9)
639 (57.8)
437 (68.4)
184 (42.1)
Primary Discharge Diagnosis Cancer
556
196 (35.3)
168 (85.7)
102 (60.7)
78 (76.5)
45 (57.7)
Endocrine
155
73 (47.1)
64 (87.7)
37 (57.8)
22 (59.5)
9 (40.9)
Cardiovascular
837
514 (61.4)
441 (85.8)
265 (60.1)
191 (72.1)
122 (63.9)
Mental disorders
570
45 (7.9)
33 (73.3)
14 (42.4)
8 (57.1)
0 (0.0)
Pulmonary
221
135 (61.1)
112 (83.0)
51 (45.5)
36 (70.6)
11 (30.6)
Gastrointestinal
481
190 (39.5)
146 (76.8)
86 (58.9)
57 (66.3)
21 (36.8)
Genitourinary
179
66 (36.9)
50 (75.8)
26 (52.0)
16 (61.5)
4 (25.0)
Orthopedics
445
120 (27.0)
92 (76.7)
41 (44.6)
31 (75.6)
12 (38.7)
Injury
1,117
287 (25.7)
226 (78.7)
95 (42.0)
62 (65.3)
28 (45.2)
Other
1,194
415 (34.8)
321 (77.3)
Prescription at Discharge§ Consult Time (minutes)II
157 (48.9)
105 (66.9)
29 (27.6)
360 (65.6)
242 (67.2)
112 (46.3)
600
NA
NA
1–5
360
NA
NA
75 (21.7)
48 (64.0)
18 (37.5)
6–10
774
NA
NA
426 (55.3)
279 (65.5)
124 (44.4)
11–15
389
NA
NA
251 (64.5)
183 (72.9)
92 (50.3)
> 15
170
NA
NA
129 (75.9)
98 (76.0)
51 (52.0)
6.1 (9.1)
6.2 (6.1)
6.4 (6.3)
6.1 (5.7)
7.4 (7.0)
Length of stay mean (SD)
5.2 (7.4)
NA, not appropriate; SD, standard deviation. * Discrepancies in column totals are due to missing values, resulting in changes in denominators. †
Three missing values.
‡
Three missing values and deletion of 91 smokers in the Other category (as shown in Table 1), none of whom had had a consult ordered.
§
Fifty-one smokers with a discharge order did not complete a consult and were removed from the denominator.
II
Five smokers in category 1–5 minutes did not complete a consult and were removed from the denominator. Three smokers in category 6–10 minutes did not complete a consult and were removed from the denominator.
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The Joint Commission Journal on Quality and Patient Safety Step 2. Consult Completion. Patients significantly more likely to have completed consults were those admitted for elective surgery and those with medication orders. There were marginally significant differences for female patients and for patients admitted for urgent medical procedures and cardiovascular disease. Step 3. Follow-Up Arranged. Female patients were significantly more likely to have a follow-up call arranged, as were patients with any admission type other than through the ED and those who had stop-smoking medications prescribed at discharge, longer tobacco cessation consultations, and/or a primary discharge diagnosis of cardiovascular disease. Patients significantly less likely to have follow-up arranged had the briefest consult times or were hospitalized for injuries. Step 4. Follow-Up Completion. Patients significantly more likely to complete follow-up were those admitted for elective surgical procedures. There was a trend toward significance for patients who had longer tobacco cessation consultations.
Characteristics of Smokers Completing All Treatment Steps As shown by the logistic regression analysis, smokers most likely to complete all treatment steps were those admitted for elective surgeries. Appendix 2 compares characteristics of smokers from each portal of admission. Smokers admitted for elective surgeries had more equal proportions of male and female patients and were much less likely to have medication orders or to have a history of mental disorders. More than half of smokers admitted for elective surgeries had discharge diagnoses of either cancer or orthopedic procedures, and more than 25% had cardiovascular conditions, gastrointestinal conditions, or injuries. Smokers completing all treatment steps also had lower rates of Medicaid and nonsponsored (uninsured) insurance coverage, were older, and had shorter hospital stays. Smokers admitted through the ED were almost twice as likely to be male, half had orders for medications to treat withdrawal, more than three quarters had a history of mental disorders, and more than 20% had a primary diagnosis of mental disorders, while nearly 20% had injuries. These patients also had the highest rates of nonsponsored coverage.
Discussion Tobacco cessation is necessary for improving many health outcomes, and providing evidence-based treatment for tobacco cessation during hospitalization is important for health care QI. The potential benefits of hospital-based services are well recognized, leading some to advocate for making services for all hosMay 2016
pitalized smokers the official “default” position.24 We undertook our data analysis after five years of implementation to help improve outcomes of our consult service. Similarly to findings of previous studies, we found that only a small proportion of smokers received all steps of evidence-based treatment. Of all smokers identified at admission at OHSU, 35.6% had a consult order, of whom only one in three was reached for follow-up—or just more than 10% of all smokers admitted. Our analysis revealed a complicated mix of patient variables, medical team practices, and hospital work-flow issues that affected how many smokers could be reached with services and follow-up. When comparing the characteristics of smokers and nonsmokers, we were not surprised to find significant differences on most variables. The significant differences between subgroups of smokers with and without consult referrals were more unexpected. These differences could suggest that medical teams were more selective in their referrals than we had previously thought. TTS observations suggested that smokers admitted under urgent conditions seemed less able to participate in tobacco cessation treatment. To test this hypothesis, we included the six admission types in comparisons of patient characteristics and in our logistic regression models. Although we did not find that urgent versus planned admissions predicted outcomes, we did find that admission type itself predicted outcomes. This was an important finding because admission types identify distinctive hospital admission portals, each with implications for hospital work flow. This finding led us to recommend system changes to potentially improving consult service performance and outcomes (see Recommendations, page 216). The finding that the largest number of smokers were admitted for injuries predominantly through the ED and trauma unit was not surprising, given that OHSU is a Level 1 trauma center. What we didn’t necessarily expect was that smokers admitted through the ED would have the lowest rates of follow-up completion and abstinence. In a study of brief ED intervention, no differences in outcomes were found between treatment and control groups, suggesting that these treatments may not be as effective for smokers seen in the ED.25 Because 783 of the total number of 2,075 smokers referred for consults were admitted through the ED, 37.8% of all orders were for patients who were less likely to be reached for follow-up and less likely to quit. As indicated by the logistic regression analysis, admission for smoking-related illnesses, Medicaid or Medicare coverage, and hospital orders for medications to treat nicotine withdrawal were predictive, as was admission for elective medical procedures in predicting which variables were most significant for Volume 42 Number 5
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The Joint Commission Journal on Quality and Patient Safety predicting completion of the first step (consult order). The initial Joint Commission tobacco measures required provision of treatment for smokers with cardiac and respiratory illnesses,* and these OHSU medical teams continue to consistently order smoking cessation treatment for their patients. A history of mental disorders was also predictive for completing a consult order, which is not surprising, given that more than two thirds of smokers admitted had a history of mental disorders. It is not clear why there were more referrals for patients covered by Medicaid or Medicare than patients with commercial coverage. It is possible that patients with commercial coverage had other resources to help them quit, so were less interested. At the second step (consult completed) only medication orders was predictive. Medication and consult orders were done at the same time, usually for patients with more significant symptoms or who requested services, or both. These were also patients generally more receptive to consult services. Greater differences appear at the third step (follow-up arranged); smokers least likely to arrange a follow-up were those with the shortest consult times and admitted with injuries. Smokers with short consult times were typically those only willing to discuss withdrawal symptoms but unwilling to discuss quitting. The majority of injured smokers seen for a consult were admitted through the ED, with only a small number admitted to the trauma unit. Smokers admitted to the trauma unit were more likely to have follow-up arranged, while injured smokers admitted through the ED were not. We are not sure why these injured ED smokers were less likely to arrange a follow-up contact. We know that some of those patients had diagnoses of mental disorders, particularly addictions, which may have led to their injuries and subsequent discharge to treatment and rehabilitation facilities, limiting their availability for follow-up. At the fourth step, only smokers admitted for elective surgeries were significantly more likely to complete a follow-up call—and were most likely to complete all the treatment steps. Because smokers completing all steps reported a 47% abstinence rate at follow-up, we were most interested in the characteristics of these smokers. When compared to all other admission types, particularly smokers admitted through the ED, smokers admitted for elective surgery were much less likely to have a history of mental disorders (52% versus 79%), a primary diagnosis of mental disorders (0.1% versus 22%) or injuries (11% versus * These measures—PN-4 Adult Smoking Cessation Advice/Counseling, HF-4 Adult Smoking Cessation Advice/Counseling, and AMI-4 Adult Smoking Cessation Advice/Counseling measures were retired effective with January 1, 2012, discharges, after the tobacco treatment measure set was introduced, because the latter apply to all patients not just subsets of patients.
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19%) and to be uninsured (9% versus 22%) than those admitted through the ED. Although medical team referral rates were about the same for both these admission types, the number of smokers referred from the ED was about 40% greater than for elective surgery. Meanwhile, close to 70% of smokers admitted for elective surgeries did not receive tobacco cessation consult orders, although their recovery might benefit from abstinence.26–28 OHSU surgeons follow best practices and recommend that their patients quit tobacco use before surgery; some will not proceed unless the patient is completely abstinent. Even so, anecdotal information suggested that OHSU surgeons were reluctant to order nicotine replacement during hospitalization because of concern that nicotine would reduce blood flow and impede recovery. With only a third of elective surgery patients receiving a medication order compared to about half for other patients, it is possible that when nicotine replacement was not ordered before surgery, neither was a bedside consultation.
Recommendations Our effort to translate the results of research studies into everyday practice met with the complicated realities of applying evidence-based protocols in an uncontrolled setting. We found that applying a standard protocol across all patient populations resulted in highly variable outcomes, with some patients more likely to be able to complete treatment than others. The proportion of total smokers admitted compared to those actually able to be followed decreased substantially, limiting the effectiveness of our consult service. Our aim was to improve referral rates and outcomes. We found identifying optimal individual patient selection criteria for referrals difficult because patient circumstances are highly variable. However, we did identify three recommendations for improvement of the referral process that could help reach those smokers more likely to benefit. 1. Limit Inappropriate Referrals. For many smokers, hospitalization may not be the best time to initiate treatment. Their medical condition and life circumstances may be too unstable to participate in treatment and follow-up. Although all hospitalized smokers deserve the opportunity to receive help to quit, the evidence base is clear that specialized services are effective only if patients receive all treatment steps, including follow-up.2 Additional education needs to be developed to help medical teams quickly assess which patients are appropriate for referrals to specialized services on the basis of available resources and which patient are not. Patients appropriate for referral are those who are willing and medically stable enough to participate in the consultation while hospitalized and also able to complete follow-up. Volume 42 Number 5
Copyright 2016 The Joint Commission
The Joint Commission Journal on Quality and Patient Safety Although all patients should receive quitting information, some patients—such as homeless patients; patients who will be discharged to prison, rehabilitation, or nursing homes; or patients without a phone—might not be able to complete follow-up. 2. Increase Referrals of Motivated Smokers. Smokers already expressing interest in quitting are routinely referred regardless of other characteristics. However, medical teams often lack good information and tools to better assess patients’ willingness to participate, opting instead to refer patients only on the basis of their medical condition. With better assessment tools, referrals for additional motivated smokers could be increased. 3. Systematically Target Groups of Smokers Who Are Most Likely to Complete Follow-Up. In our analysis, these were smokers with elective surgical admissions. Because these surgeries are scheduled, patients can be systematically identified and their surgical teams prompted to refer more patients. Further, because many of those patients are first seen in the presurgery clinic, tobacco cessation treatment can be introduced before hospitalization,27 with follow-up during hospital stays. Although optimal timing for smoking cessation before surgery is uncertain, adding this treatment step could have the effect of improving cessation rates as well as surgical outcomes.
Strengths and Limitations This study is one of the first studies to report on the longterm experience of a hospital-based tobacco cessation service. Most published reports are based on the results of research studies. A strength of this analysis is that the OHSU population of smokers is large and data were collected for more than two years. This provides a comprehensive picture of implementation over time and a more complete description of the patient population and referral patterns that affect outcomes at OHSU. Even so, as a regional academic medical center, the population of OHSU smokers and referral patterns may differ from those at other hospitals. EHR data on race was incomplete and not included in this data set, limiting generalizability to other hospital settings. We also do not have reportable data on readiness to quit for all smokers, limiting our ability to identify motivation among patient group and link to outcomes. Because the only patients seen were those referred for services, the OHSU data reflect these referral decisions. Follow-up was limited to two to three weeks postdischarge because of limited resources. With a longer duration, different patterns of follow-up contact could emerge. Even so, our analysis has yielded new insights into addressing QI for our hospital-based services and could serve as an example for other hospitals seeking to improve their tobacco cessation services. May 2016
Conclusion Studies of tobacco cessation services for hospitalized patients have shown the potential for improving patient health outcomes. This study adds important information about the effects of real-world patient characteristics, medical team referrals, and the ability of patients to complete all steps of evidence-based treatment. Although developing the capacity to offer all hospitalized patients treatment for tobacco dependence may be optimal, typically only a small percentage of smokers are able to be reached. By adopting a more strategic approach to patient referrals, particularly with limited resources, the number of smokers reached could be increased, leading to improvements in both treatment quality and treatment outcomes. J This work was performed at Oregon Health & Science University Hospitals, Portland, Oregon, and supported by OHSU hospitals and the OHSU Division of Pulmonary and Critical Care Medicine. Wendy Bjornson reports unrestricted educational grants from Pfizer and ownership of 5 shares of Pfizer stock. David Gonzales reports research grants from Pfizer and Nabi Biopharmaceuticals; honoraria from GlaxoSmithKline and Gilead Sciences; and ownership of five shares of Pfizer stock.
Wendy G. Bjornson, MPH, and David H. Gonzales, PhD, are Co– Directors, Oregon Health & Science University (OHSU) Smoking Cessation Center, Division of Pulmonary and Critical Care Medicine, School of Medicine, OHSU, Portland, Oregon. Catherine J. Markin, MD, formerly Medical Director, OHSU Smoking Cessation Center, is Medical Director, Pulmonary and Sleep Services, Legacy Medical Group, Legacy Health, Portland, Oregon. Noal Clemons, BA, formerly Senior Research Assistant, OHSU Smoking Cessation Center, is Senior Research Assistant, Oregon Institute of Occupational Health Sciences, OHSU. Frances Favela, MPH, was Research Associate, OHSU Smoking Cessation Center. Trisha M. Coleman, MA, was Research Associate, OHSU Smoking Cessation Center. Caroline Koudelka, MPH, is Research Associate, and Jodi A. Lapidus, PhD, is Professor, Biostatistics & Design Program, OHSU-Portland State University (PSU) School of Public Health. Please address correspondence to Wendy Bjornson, [email protected].
Online Only Content http://www.ingentaconnect.com/content/jcaho/jcjqs See the online version of this article for Appendix 1. Summary of Odds Ratios for Significant Variables included in Analysis at Each Step of Consult Completion Appendix 2. Significant Differences in Proportions or Means of Demographic Variables for Elective Surgical Admission Type Compared to Other Admission Types
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The Joint Commission Journal on Quality and Patient Safety References
1. McBride CM, Emmons KM, Lipkus IM. Understanding the potential of teachable moments: The case of smoking cessation. Health Educ Res. 2003;18:156–170. 2. Rigotti NA, Munafo MR, Stead LF. Interventions for smoking cessation in hospitalised patients. Cochrane Database Syst Rev. 2007 Jul 18;3:CD001837. 3. Mohiuddin SM, et al. Intensive smoking cessation intervention reduces mortality in high–risk smokers with cardiovascular disease. Chest. 2007;131: 446–452. 4. Quist-Paulsen P, Gallefoss F. Randomised controlled trial of smoking cessation intervention after admission for coronary heart disease. BMJ 2003 Nov 29;327:1254–1257. 5. Colivicchi F, et al. Effect of smoking relapse outcome after coronary syndromes. Am J Cardiol. 2011 Sep 15;108:804–808. 6. Smith PM, Burgess E. Smoking cessation initiated during hospital stay for patients with coronary artery disease: A randomized controlled trial. CMAJ. 2009 Jun 23;180:1297–1303. 7. Rigotti NA. Integrating comprehensive tobacco treatment into the evolving US healthcare system: It’s time to act. Arch Intern Med. 2011 Jan 10;171:53–55. 8. The Joint Commission. Tobacco Treatment (TOB) National Hospital Inpatient Quality Measures. In Specifications Manual for National Hospital Inpatient Quality Measures, version 5.0b: Discharges 10-01-15 (4Q15) Through 06-30-16 (2Q16). Oak Brook, IL: Joint Commission Resources, 2015. Accessed Mar 23, 2016. http://www.jointcommission.org/assets/1/6/NHQM_v5_0_b_PDF_11 _13_2015.zip. 9. Fiore MC, et al. Treating Tobacco Use and Dependence: 2008 Update. Clinical Practice Guideline. Rockville, MD: US Department of Health and Human Services, Public Health Service, 2008. 10. HealthIT.gov. EHR Incentives & Certification: EHR Incentive Programs: Medicare and Medicaid EHR Incentive Programs. (Updated: Jan 15, 2013.) Accessed Mar 24, 2016. https://www.healthit.gov/providers-professionals /ehr-incentive-programs. 11. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010 Aug 5;363:501–504. 12. Partnership for Prevention. Helping Patients Quit: Implementing the Joint Commission Tobacco Measure Set in Your Hospital. Accessed Mar 24, 2016. https://www.prevent.org/downloadStart.aspx?id=54. 13. Smoking Cessation Leadership Center. Destination Tobacco-Free: A Prac-
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tical Tool for Hospitals and Health Systems. 2013. Accessed Mar 24, 2016. http://smokingcessationleadership.ucsf.edu/resources/toolkits. 14. Smith PM, Taylor CB. Implementing an Inpatient Smoking Cessation Program. Mahwah, NJ: Lawrence Erlbaum Associates, 2006. 15. Duffy SA, et al. Implementation of the Tobacco Tactics program in the Department of Veterans Affairs. J Gen Intern Med. 2010;25 Suppl 1:3–10. 16. Partnership for Prevention. Working with Healthcare Delivery Systems to Improve the Delivery of Tobacco-Use Treatment to Patients. Apr 2008 (Updated: Apr 2009.) Accessed Mar 24, 2016. http://www.prevent.org/download Start.aspx?id=23. 17. Gadomski AM, et al. Effectiveness of an inpatient smoking cessation program. J Hosp Med. 2011;6:E1–8. 18. Faseru B, et al. Evaluation of a hospital–based tobacco treatment service: Outcomes and lessons learned. J Hosp Med. 2011;6:211–218. 19. Fung PR, et al. Effectiveness of hospital-based smoking cessation. Chest. 2005;128:216–223. 20. Oregon Health Authority. Quit for Life® Program. About the Program. Accessed Mar 24, 2016. https://www.quitnow.net/oregon/About/. 21. Smith PM, et al. In–hospital smoking cessation programs: Who responds, who doesn’t? J Consult Clin Psychol. 1999:67:19–27. 22. Faseru B, et al. Prevalence and predictors of tobacco treatment in an academic medical center. Jt Comm J Qual Patient Saf. 2009;35:551–557. 23. Benjamini, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995;57:289–330. 24. Richter KP, Ellerbeck EF. It’s time to change the default for tobacco treatment. Addiction. 2015;110:381–386. 25. Katz DA, et al. The emergency department action in smoking cessation (EDASC) trial: Impact on cessation outcomes. Nicotine Tob Res. 2013;15: 1032–1043. 26. Cropley M, et al. The effectiveness of smoking cessation interventions prior to surgery: A systematic review. Nicotine Tob Res. 2008;10:407–412. 27. Møller AM, et al. Effect of preoperative smoking intervention on postoperative complications: A randomised clinical trial. Lancet. 2002 Jan 12:359: 114–117. 28. Thomsen T, Villebro N, Møller AM. Interventions for preoperative smoking cessation. Cochrane Database Syst Rev. 2014 Mar 27;3:CD002294.
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Appendix 1. Summary of Odds Ratios for Significant Variables Included in Analysis at Each Step of Consult Completion 1. Consult Referral OR (95% CI)
OR (95% CI)
3. Follow-Up Arranged OR (95% CI)
p Value*
p Value*
4. Follow-Up Completion OR (95% CI)
p Value*
1.06 (0.94–1.20)†
0.3219
1.36 (1.07–1.74)
0.0575
1.34 (1.07–1.67)†
0.0171
1.20 (0.88–1.63)†
0.2522
1.00 (1.00–1.01)†
0.8367
1.01 (1.00–1.02)
0.2113
1.00 (1.00–1.01)†
0.3424
1.01 (1.00–1.02)
0.1270
2.41 (1.64–3.55) 0.83 (0.69–0.99) 0.69 (0.51–0.94) 1.09 (0.93–1.28) 0.98 (0.75–1.26)
< 0.0001
1.52 (0.79–2.94) 1.52 (1.07–2.17) 0.91 (0.47–1.73) 1.48 (1.09–2.01) 1.07 (0.67–1.69)
0.3068
2.44 (1.34–4.45) 1.56 (1.13–2.16) 2.54 (1.30–4.96) 1.50 (1.14–1.99) 1.78 (1.13–2.79)
0.0117
2.28 (1.04–5.01) 2.41 (1.55–3.77) 0.77 (0.35–1.70) 0.95 (0.66–1.38) 0.89 (0.50–1.58)
0.0975
Cessation Medication Order Yes vs. No
NA
NA
2.04 (1.62–2.57)
< 0.0001
1.58 (1.26–1.99)
0.0006
0.88 (0.59–1.32)†
0.5443
History of Mental Disorders Yes vs. No
1.53 (1.34–1.75)
< 0.0001
1.30 (1.01–1.68)
0.1270
0.94 (0.73–1.22)†
0.6523
1.37 (0.96–1.95)
0.1270
Insurance Status Medicaid vs. Commercial/Contract Medicare vs. Commercial/Contract Nonsponsored vs. Commercial/Contract Other sponsored vs. Commercial/Contract
1.24 (1.04–1.47) 1.32 (1.11–1.56) 1.22 (1.00–1.49) 1.26 (0.95–1.67)
0.0268
0.77 (0.54–1.09)† 0.76 (0.52–1.10)† 0.87 (0.58–1.30)† 0.73 (0.42–1.27)†
0.1395†
1.30 (0.94–1.79)† 1.16 (0.83–1.62)† 1.33 (0.92–1.94)† 0.99 (0.57–1.72)†
0.1127†
0.81 (0.51–1.29)† 0.81 (0.50–1.32)† 0.92 (0.54–1.57)† 0.40 (0.19–0.86)†
0.3748†
Sex
Male vs. Female
p Value*
2. Consult Completion
Patient Age (per year increase) Admission Type Elective Medical vs. Emergent Elective Surgical vs. Emergent Trauma vs. Emergent Urgent Medical vs. Emergent Urgent Surgical vs. Emergent
0.0572 0.0344 0.3618 0.8562
0.0037 0.0724 0.1353
0.0102 0.8272 0.0575 0.7807
0.1420† 0.4968† 0.2645†
0.0152 0.0152 0.0117 0.0202
0.3808† 0.1344† 0.9811†
0.0013 0.6831 0.8167 0.8167
0.3914† 0.7546† 0.0195†
(continued on page AP2)
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Appendix 1. Summary of Odds Ratios for Significant Variables Included in Analysis at Each Step of Consult Completion (continued) 1. Consult Referral OR (95% CI) Primary Discharge Diagnosis Yes vs. No Cancer Endocrine Cardiovascular Mental disorders Pulmonary Gastrointestinal Genitourinary Orthopedic Injury Other Prescription at Discharge Yes vs. No
1.27 (1.03–1.56) 1.78 (1.28–2.47) 3.52 (2.99–4.15) 0.10 (0.07–0.14) 3.10 (2.33–4.12) 1.37 (1.12–1.67) 1.27 (0.92–1.74) 0.90 (0.71–1.14) 0.73 (0.61–0.86) 0.97 (0.84–1.13)
p Value*
0.0457 0.0015 < 0.0001 < 0.0001 < 0.0001 0.0040 0.1764 0.4264 0.0008 0.7703
2. Consult Completion OR (95% CI)
p Value*
1.1 (0.70–1.73 1.72 90.84–3.52) 1.52 (1.21–2.07) 0.59 (0.30–1.18) 1.16 (0.72–1.88) 0.80 (0.55–1.15) 0.67 (0.37–1.21) 0.73 (0.45–1.17) 1.01 (0.71–1.45 0.71 (0.53–0.94)
0.8140 0.2640 0.0575 0.2640 0.6771 0.3068 0.3068 0.3068 0.9481 0.0630
3. Follow-Up Arranged OR (95% CI)
p Value*
OR (95% CI)
p Value*
1.18 (0.80–1.74) 1.47 (0.85–2.54) 1.57 (1.21–2.03) 0.64 (0.29–1.40) 0.87 (0.57–1.32) 1.40 (0.96–2.05) 1.13 (0.62–2.08) 0.63 (0.39–1.02) 0.59 (042–0.83) 0.76 (0.57–1.00)
0.4389
0.94 (0.54–1.65)†
0.8418†
0.2103
0.80 (0.40–1.62)† 1.35 (0.93–1.98)†
0.5354†
0.51 (0.16–1.64)† 1.15 (0.60–2.22)† 0.81 (0.49–1.34)† 0.73 (0.31–1.69)† 0.79 (0.35–1.78)† 1.10 (0.64–0.91)† 0.92 (0.60–1.39)†
0.2562†
0.0029 0.3124 0.5352 0.1047 0.6848 0.0836 0.0100 0.0709
NA
NA
NA
NA
NA
NA
Contact Time (minutes): 1–5 vs. 6–10
NA
NA
NA
NA
< 0.0001
11–15 vs. 6–10
NA
NA
NA
NA
> 15 vs. 6–10
NA
NA
NA
NA
0.23 (0.17–0.31 ) 1.42 (1.09–1.84) 2.39 (1.62–3.53)
1.03 (1.02–1.04)
< 0.0001
1.00 (0.99–1.01)†
0.7707
1.01 (0.99–1.03)†
0.3733
Length of Stay (per-day increase)
4. Follow-Up Completion
0.0171 0.0001
0.74 (0.54–1.01)
0.1194†
0.6722† 0.4090† 0.4635† 0.5757† 0.7295† 0.6748†
0.1136
0.94 (0.54–1.62) 1.46 (1.02–2.08) 1.81 (1.13–2.91)
0.8167
0.98 (0.95–1.00)
0.0975
0.0975 0.0830
OR, odds ratio; CI, confidence interval; NA, use of the variable was not appropriate or not available for the indicated stage of analysis. * p values for odds ratios of individual comparisons are adjusted for multiple comparisons by false discovery rate (FDR) method. † Variables not found to be predictive (p > 0.1000) were removed from final models. Unadjusted model p values and odds ratios of these variables are reported.
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Appendix 2. Significant Differences in Proportions or Means of Demographic Variables for Elective Surgical Admission Type Compared to Other Admission Types* Urgent Medical
Elective Surgical
Elective Medical
Emergent
Trauma
Urgent Surgical
n
%
p Value†
n
%
n
%
n
%
n
%
n
%
SexFemale Male
707 687
50.7% 49.3%
< 0.0001
667 561
54.3 45.7
81 53
60.4 39.6
832 1,476
36.0 64.0
76 268
22.1 77.9
141 184
43.4 56.6
Insurance Class Commercial/Contracts Medicaid Medicare Nonsponsored
464 333 412 126
33.3 23.9 29.6 9.0
< 0.0001
18.1 39.7 24.8 11.9
46 39 45 3
34.3 29.1 33.6 2.2
350 669 630 497
15.2 29.0 27.3 21.5
150 52 33 64
24.3 24.6 29.8 16.0
5.5
1
0.7
162
7.0
45
43.6 15.1 9.6 18.6 13.1
79 80 97 52
Other sponsored
222 487 305 146 68
17
5.2
59
4.2
History of Mental Disorders
723
52.1
< 0.0001
878
72.3
96
71.6
1,803
78.1
209
60.8
225
69.2
Cessation Medication Order
469
33.6
< 0.0001
571
46.5
69
51.5
1,225
53.1
151
43.9
138
42.5
Primary Discharge Diagnosis‡ Cancer Endocrine Cardiovascular Mental disorders Pulmonary Gastrointestinal Genitourinary Orthopedics Injury Other
398 28 126 1 17 105 60 348 154 150
28.7 2.0 9.1 0.1 1.2 7.6 4.3 25.1 11.1 10.8
< 0.0001
59 28 327 34 39 100 24 21 129 453
4.9 2.3 26.9 2.8 3.2 8.2 2.0 1.7 10.6 37.3
18 1 31 10 0 4 0 0 0 70
13.4 0.7 23.1 7.5 0.0 3.0 0.0 0.0 0.0 52.2
46 91 236 509 157 228 91 64 432 443
2.0 4.0 10.3 22.2 6.8 9.9 4.0 2.8 18.8 19.3
2 1 3 11 1 0 1 1 319 5
0.6 0.3 0.9 3.2 0.3 0.0 0.3 0.3 92.7 1.5
21 5 100 2 6 38 3 11 77 59
6.5 1.6 31.1 0.6 1.9 11.8 0.9 3.4 23.9 18.3
Prescription at Discharge
135
9.7
0.2946
161
13.1
34
25.4
206
8.9
20
5.8
42
12.9
63 172 91 45
17.0 46.4 24.5 12.1
0.0773
94 223 107 45
20.0 47.5 22.8 9.6
11 24 22 9
16.7 36.4 33.3 13.6
159 274 127 54
25.9 44.6 20.7 8.8
14 26 12 3
25.5 47.3 21.8 5.5
19 54 30 13
16.4 46.6 25.9 11.2
Mean
SD
p Value§
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Consult Time (minutes) 1–5 6–10 11–15 > 15
Age in Years
51.6
14.2
< 0.0001
46.2
16.6
46.9
15.1
45.9
14.9
41.2
15.7
51.4
15.4
Length of Stay
4.1
4.5
< 0.0001
5.6
5.9
5.2
5.6
5.2
7.0
5.8
10.2
8.5
16.3
SD, standard deviation. * Discrepancies for mental disorders compared to values in Table 1 due to missing values; total denominator = 3,934, that is, with the subtraction of the 40 missing values (3 from admission type and 37 from the “other” category). Resulting denominators for the admission type are 1,389 (5) for elective surgical, 1,215 (13) for urgent medical, 2,297 (11) for emergent, and 323 (2) for urgent surgical. †
Chi-square p value compares proportions of admission type subgroup to all other subgroups combined.
‡
Discrepancies in column totals for primary discharge diagnosis compared to Table 1 are due to missing values. The resulting denominators are 1,387 (missing 7) for elective surgical,1,214 (14) for urgent medical, 2,297 (11) for emergent, and 322 (3) for urgent surgical.
§
p values compare mean/median of admission type subgroup to all other subgroups combined by t-test (age) and Wilcoxon rank-sum test (length of stay).
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