South dakota statewide nursing minimum data set project

South Dakota Statewide Nursing Minimum Data Set Project KATHRYN L. KARPIUK,

MNE, RN,* CONNIE W H I T E DELANEY, PHD, RN,t AND POLLY RYAN, PHD, RN$

The elements of the Nursing Minimum Data Set (NMDS) were collected manually from 188 medical records in eight acute care facilities. These eight facilities represent 54 per cent of the beds in South Dakota. The purpose of the study was to describe discharge destination, nursing diagnoses, nursing interventions, and nursing resource utilization for patients with fractured femur with pinning. The sample was primarily female (69.1 per cent), with a mean age of 78.5 years. Most (84.0 per cent) patients were transferred to another facility, with 46.2 per cent going to extended care facilities. The most frequent nursing diagnoses were comfort (89.9 per cent) and physical mobility (59.6 per cent). Interventions were classified using the 16-category classification scheme developed by Werley and Lang. The most frequently recorded types of interventions were in the category of monitoring and/or surveillance (16.7 per cent of 7,555 interventions), whereas emotional support and/or counseling was much less frequent (3.0 per cent of 7,555). Discharge planning was the most frequent nursing intervention in the category of coordination and collaboration of care (54.8 per cent of 188 patients). Documentation systems have been structured to accommodate technical tasks on flow sheets, for example. Nursing resource utilization was the most difficult, and also presently the least meaningful, NMDS element to collect because each facility has different staffing, different patient classification systems, and no prescribed method for collecting these data. Manual data collection is time-consuming and expensive and therefore not recommended. (Index words: Acute care; Fractured femur with pinning; Manual data collection; Nursing Minimum Data Set; South Dakota Statewide Project) J Prof Nurs 13:76-83, 1997. Copyright © 1997 by W.B. Saunders Company

*EducationSpecialist,NursingProjects,Educationand Development Center,SiouxValleyHospital, SiouxFalls, SD. "~AssociateProfessor, College of Nursing, The Universityof Iowa, IowaCity, IA. ~:Collaborative Care Nurse Researcher, St. Joseph's Hospital, Milwaukee,WI. Supported in part by the American Organization of Nurse Executiveswith a grant throughthe AmericanNursesFoundation, 1993-1994. Address correspondenceand reprint requests to Ms Karpiuk: SiouxValleyHospital, Educationand DevelopmentCenter, 1100 S EuclidAve,Box 5039, SiouxFalls, SD 57117-5039. Copyright© 1997 byW.B. SaundersCompany 8755-7223/97/1302-0007503.00/0 76

HE INTENSITY and complexity of nursing care required for comprehensive quality patient care continues to increase. Concurrently, information systems have been implemented to capture and process data to meet the demands for quality, comprehensive, and appropriate nursing care. Most information systems and approaches, however, have used nonnursing data sets to address nurses' work. These approaches have assumed that nursing care evolves from medical orders rather than recognizing that nurses have their own information on which to base clinical judgments. The purpose of this study was to collect clinical data using the Nursing Minimum Data Set (NMDS) (Werley & Lang, 1988) framework. Data were collected from patients who had a fractured femur with pinning (open reduction, internal fixation [ORIF], International Classification of Diseases-9 [ICD-9] code 79.35 [Puckett, 1994]). Data were collected across a rural state in eight acute care hospitals that ranged in size from 25 to 503 beds. The specific goal was to describe discharge destination, nursing diagnoses, nursing interventions, and nursing resource utilization for patients with fractured femur with pinning.

T

Background

Nursing practice consists of nurses' abilities to diagnose clients' actual or potential health deficits, establish nurse-sensitive patient outcomes, select and implement effective and efficient nursing interventions, and evaluate the clients' achievement of the desired outcomes. To describe nursing practice, specific and unique pieces of information, representing nursing's essential data, are required. These elements are included in the NMDS. Built on the concept of the Uniform Minimum Health Data Set, the NMDS includes elements of the Uniform Hospital Discharge Data Set (UHDDS) (Health Information Policy Council, 1984) and four nursing elements. The NMDS is a standardized approach that facilitates the abstraction

Journal ofProfessionalNursing,Vol 13, No 2 (March-April),

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SD STATEWIDE NURSING MINIMUM DATA SET PROJECT

of essential, core minimum data to describe nursing practice. It is intended for use in any setting where nursing care is provided (Werley, Ryan, Zorn, & Devine, 1994). Since 1991 the American Nurses Association (ANA) has endorsed using the NMDS in health care information systems (ANA, 1991; McCormick, et al., 1994). Research using the elements of the NMDS has begun to emerge. Delaney and colleagues from The University of Iowa have the largest program of NMDS research. Nine studies have been conducted to estimate NMDS availability in acute care sites. They were undertaken to estimate the cost-effectiveness and to test the research utility of electronic data retrieval; to test transfer methodology; to allow nurses to validate the defining characteristics of North American Nursing Diagnosis Association diagnoses retrospectively; and to establish a patient care profile of ICD-9 codes, diagnosis-related groups (DRG) categories, and nursing diagnosis categories (Delaney, 1991; Mehmert & Delaney, 1991; Mehmert, Delaney, Prophet, & Crossley, 1994; Rios, Delaney, Kruckeberg, Chung, & Mehmert, 1991). Ryan and Delaney (1995) reviewed NMDS-related research and concluded that the NMDS is available in both computerized and noncomputerized systems. However, a higher percentage of elements is available from the computerized system. Information retrieval is possible from both types of systems, but it is less costly from computerized systems. They concluded that the NMDS has been used to describe nursing practice in hospitals, community nursing centers, and home health care. Data from more than one institution have been combined, but this South Dakota study is unique because it combines data from eight hospitals in one state and describes nursing practice in a rural state. Method PARTICIPANTS

Eight hospitals participated, including the three largest hospitals, representing 14.0 per cent of the acute care facilities. Of the state's 57 hospitals, 47 have less than 50 beds. All facilities were Medicare certified and participated in the Blue Cross plan. Seven were nongovernmental, not-for-profit facilities; the eighth facility was city owned. All facilities were short-term stay, general medical, and surgical acute care hospitals. The eight hospitals represented a total of 1,771 (54 per cent) of the 3,305 hospital beds in South Dakota (Table 1).

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TABLE 1. Facilities Participating

Facility

City

St. Michael's Brookings St. Mary's Queen of Peace St. Luke's Midland Rapid City Regional McKennan Sioux Valley

Tyndall Brookings Pierre Mitchell Aberdeen

Pa~ of Average Pilot ORIF/ Beds Study Year*

Years Data From

25 53 86 96 225

Yes Yes Yes Yes Yes

3.6 8.1 10.2 34.8 61.4

1990-1993 1990-1994 1991-1994 1993 1993

Rapid City 376 Sioux Falls 407 Sioux Falls 503

No Yes Yes

101.8 79.3 84.4

1994 1993-1994 1993

Abbreviation: ORIE open reduction, internal fixation. *Average during last 3 to 5 years.

All facilities used NANDA nomenclature for standardizing the documentation of nursing diagnoses. In a conversation with K. Heligas (Executive Director, South Dakota Nurses' Association, personal communication, January 11, 1995), seven major continuing education programs on nursing diagnosis were provided between 1986 and 1992. Nurses' expertise with documenting nursing diagnoses, as well as the internal monitoring of accuracy from facility to facility, varied widely. Nursing documentation in the patient record varied from computerized systems to manual systems. Interventions and intensity of nursing care were not standardized among the facilities. Outcomes were either resolved or unresolved nursing diagnoses or were unrecorded. Data were collected from a convenience sample of 188 medical records. Seven hospitals collected data from 25 records; the eighth collected data from 13 records. A sample of 25 records was selected starting with the most recent and proceeded backward in time. The majority of records were from the period of 1993 to 1994. However, some records dated back to 1990. This range in time occurred because smaller facilities did not have enough records during 1 year. The average annual number of patients with ORIF and the years of records reviewed by each hospital are shown in Table 1. In some facilities nursing diagnoses and interventions were selected from computerized lists. Nursing interventions were not always linked to particular diagnoses. In addition, physicians prescribed interventions that were not linked to a nursing plan of care. INSTRUMENT

The entire Data Collection Instrument for Inpatient Acute Care (tool) (Werley, Devine, & Zorn, 1990) was used. It had the following three sections:

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KARPIUK, DELANEY, AND RYAN

(1) patient demographic and service data (UHDDS data; primary payer; medical diagnoses; and DRG code); (2) nursing care data (diagnoses, interventions, outcomes, and intensity of nursing care); and (3) additional information (primarily data collection time). For the purposes of this study, two modifications were made to the tool. First, discharge data were made more specific by adding two categories: rehabilitation and swing bed (hospital bed that may be used flexibly as long-term care bed). Second, based on a recommendation from Ryan et al. (1994), data for nursing care hours and staff mix, rather than length of stay, were collected for each 24-hour period under review. PROCEDURE

The proposal was reviewed for protection of subjects by an institutional review board when available. Permission to conduct the study was obtained from hospital administration. Patient confidentiality was maintained by omitting identifying patient data; neither the medical record number nor the social security number was used to identify patients. Nine registered nurses (RNs) collected data in the eight facilities (one site had two data collectors). Their training was facilitated by the Rural Development Telecommunications Network, which provided an innovative communication link among settings through videoconferencing with two-way audio and two-way video so that there was face-to-face communication. Before the teleconference, data collectors received the tool with directions, an example of UHDDS data, and background material. During the teleconference the tool and coding were explained. Data were collected using the NANDA classification for nursing diagnoses (Doenges & Moorhouse, 1993) and the 16-category classification for nursing interventions (Werley and Lang, 1988), developed at the teleconference. Intensity of nursing care data were collected by

the agencies in whatever manner the agency could provide it in 24-hour segments. The data collector was required to classify interventions documented in the medical record into one of the 16 categories. Care was taken to assure interrater reliability among the data collectors. First, the group discussed 86 specific interventions during the teleconference and agreed by consensus on the placement of each intervention within one of the categories. (The 86 interventions were the nursing interventions identified in a 1991 pilot study; seven of the eight facilities in this study participated in the pilot study; see table 1.) A few weeks later a resorted list of the same 86 interventions was mailed to each data collector. Each data collector independently coded the nursing interventions into one of the categories. An alpha coefficient of 0.74 was obtained. In addition, the classification of each intervention into a category was reviewed by the primary investigator, and discrepancies in judgment were resolved. Data were entered into Excel (Microsoft Corp, 1985-1993) at Sioux Valley Hospital and sent to The University of Iowa for analysis. Results DEMOGRAPHIC ELEMENTS

The data were analyzed using descriptive statistics (Table 2). The sample of 188 patients consisted of 130 (69.1 per cent) women and 58 (30.9 per cent) men. Their age ranged from 18 to 100 years, with a mean of 78.5 years; 13 (6.9 per cent) were younger than 51 years; and 152 (80.9 per cent) were older than 70 years. The mode was 89 years; 91 (48.4 per cent) patients were between 81 and 90 years. Missing data accounted for 26.1 per cent of race and 65.4 per cent of ethnicity data: 133 (70.7 per cent) were white; 65 (34.6 per cent) were not of Spanish/Hispanic origin. Mean length of stay for hospitalization was 8.3 days

TABLE 2. Compiled Demographic Data (n = 188) Ethnicity

No. (%)

Race

No, (%)

Age (yr)

No. (%)1

LOS* (d)

Not Spanish/Hispanic Missing

65 (34.6) 123 (65.4)

White Asian Native American Missing Not recorded

133 (70.7) 1 (0.5) 5 (2.7) 3 (1.6) 46 (24.5)

18-40 41-50 51-60 61-70 71-80 81-90 91-100 Mean

9 (4.8) 4 (2.1) 7 (3.7) 16 (8.5) 42 (22.3) 91 (48.4) 19 (I0.1) 78.5

2-3 4-5 6-7 8-9 10-11 12-13 14-32 Mean

Abbreviation: LOS, length of stay. *LOS in the acute care facility during which the data were collected. ?Does not equal 100% because of rounding.

No. (%)1 6 30 60 38 29 14 11

(3.2) (16.0) (31.9) (20.2) (15.4) (7.5) (5.9) 8.3

Discharge Destination Home Transferred to Acute Extended care Rehabilitation Swing bed Total transfers Died

No. (%) 28 (14.9) 7 (3.7) 73 (38.8) 30 (16.0) 48 (25.5) 158 (84.0) 2 (1.1)

SD STATEWlDENURSING MINIMUM DATASET PROJECT

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(median, 7.5; range, 2 to 32; SD = 3.8). Onty 28 (14.9 per cent) patients were discharged to their home. Of the 158 patients transferred, 73 (46.2 per cent) went to extended care facilities, 48 (30.4 per cent) went to a swing bed, 30 (19.0 per cent) went to rehabilitation, and 7 (4.4 per cent) went to another acute care facility (Table 2). Medicare was the primary payer of the hospital bill for 160 (85.1 per cent) patients; insurance covered 14 (7.5 per cent) patients. There were 900 primary and secondary medical diagnoses. The mean was 5.5 (SD = 2.7; median, 5). The most frequent of the 205 different secondary diagnoses are listed in Table 3. For all but 10 (5.3 per cent) patients, the principle admission and first diagnoses were variations of ICD-9 codes 820 and 821 (hip fracture). The most frequent DRGs, 210 and 211 (hip and femur procedures), were present in 166 (88.3 per cent) records. NURSING DIAGNOSES

There was a total of 744 nursing diagnoses, with a range of 0 to 12 diagnoses recorded. Only 6 (3.2 per cent) patients had no nursing diagnoses. Ten (5.3 per cent) patients had more than 8 diagnoses; the mean was 4, and the mode was 2. The most frequent TABLE3. Most Frequent Medical Diagnoses (n = 188) ICD-9 Medical Diagnosis

1. Pertrochanteric fracture, closed, intertrochanteric section 2. Acute posthemorrhagic anemia 3. Essential hypertension, unspecified 4. Other disorders of urethra and urinary tract 5. Pertrochanteric fracture, closed, subtrochanteric section 6. Heart failure 7. Fracture of neck of femur, transcervical, closed, other 8. Fracture, unspecified part of neck of femur, closed 9. Chronic airway obstruction, not elsewhere classified 10. Unspecified hypothyroidism 11. Other forms ef chronic ischemic heart disease 12. Diabetes mellitus 13. Hypopotassemia 14. Anemia, unspecified 15. Other cerebral degenerations 16. Pneumonia, organism unspecified 17. Other emphysema

No.

No. (%)*

820.21 285.1 401.9

117 (62.2) 46 (24.5) 27(14.4)

TABLE 4.

Most Frequent Nursing Diagnoses (n = 188) Nursing Diagnosis (Broad Category/Specific)

No. (%)*

Comfort Pain, acute Pain Alteration in comfort Physical mobility Impaired physical mobility Alteration in physical mobility Impaired tissue integrity Impaired skin integrity Impaired skin integrity, potential for Impaired skin integrity, high risk Tissue perfusion Alteration in tissue perfusion, peripheral Altered tissue perfusion Altered thought processes Potential for infection High risk for trauma Knowledge deficit Fluid volume, excess, high risk Anxiety

169 (89.9) 108 (57.5) 50 (26.6) 11 (5.9) 112 (59.6) 86 (45.7) 26 (13.8) 87 (46.3) 47 (25.0) 23 (12.2) 17 (9.0) 66 (35.1) 48 (25.5) 18 (9.6) 42 (22.3) 41 (21.8) 38 (20.2) 30 (16.0) 22 (11.7) 22 (11.7)

*Patients usually had more than one nursing diagnosis; the column totals more than 100%.

NANDA diagnoses were acute pain and impaired physical mobility. As identified in Table 4, 169 (89.9 per cent) patients had a diagnosis related to comfort, and 112 (59.6 per cent) patients had a diagnosis related to physical mobility. The most frequent diagnosis related to comfort was acute pain followed by pain and alteration in comfort. Two NANDA diagnoses related to physical mobility, impaired physical mobility and alteration in physical mobility, were among the 10 most frequent diagnoses. The most frequent diagnosis related to tissue integrity was impaired skin integrity followed by potential for impaired skin integrity and high risk for impaired skin integrity. Of the 2 NANDA diagnoses related to tissue perfusion, the most frequent was alteration in peripheral tissue perfusion followed by altered tissue perfusion.

599

25 (13.3)

820.22 428

17 (9.0) 15 (8.0)

820.09

15 (8.0)

820.8

15 (8.0)

NURSING OUTCOMES

496 244.9

12 (6.4) t 1 (5.9)

414 250 276.8 285.9 331 486 492.8

10 (5.3) 8 (4.3) 8 (4.3) 8 (4.3) 8 (4.3) 8 (4.3) 8 (4.3)

The outcomes of the 744 nursing diagnoses were mixed, with 402 (54.0 per cent) resolved, 271 (36.4 per cent) unresolved, 54 (7.3 per cent) not recorded, and 17 (2.3 per cent) missing. The 6 most frequently unresolved nursing diagnoses (comprising 202 diagnoses) were peripheral tissue perfusion (n = 44), impaired mobility (n = 43), acute pain (n = 34), altered thought processes (n = 31), high risk for infection (n = 26), and impaired skin integrity (n = 24). The other 27 unresolved diagnoses occurred less frequently (12 times or less).

Abbreviation: ICD-9, International Classification of Diseases-9. *A maximum of four medical diagnoses per patient; totals more than 100%.

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KARPIUK, DELANEY,AND RYAN NURSINGINTERVENTIONS

There were 1,862 types of nursing interventions linked to nursing diagnoses and an additional 5,693 interventions not linked to a diagnosis. The most frequent of all types of interventions (Table 5) were in the classifications of monitoring and/or surveillance (n = 1,262), applications and/or treatments (n = 1,048), and teaching (n = 790). NURSING INTENSITY

The hours of care were not available for all facilities nor for all patients; reported data were from 99 patients in seven facilities. The range of time for care provided to patients was 1.2 hours to 18.0 hours/day (full days and excluding days of admission and

discharge). Care mix models ranged from entirely RNs to RNs, licensed practical nurses (LPNs), and nursing assistants (aides). Most care (n = 99) was provided by RNs (5,019.7 hours); LPNs provided 490.9 hours; aides provided 1,599.0 hours. The hours of care generally gradually decreased from early in the admission period to the lowest amount of time on the day of discharge. The day of admission, usually comprising less than 24 hours, had fewer nursing care hours than the next several days. The RN hours of care decreased more rapidly than the aide hours of care. DATA COLLECTION TIME

Data collection time per medical record for all data ranged from 0.3 to 3.8 hours (mean, 1.6; median, 1.2;

TABLE5. Most Frequent Types of Nursing Interventions in the 16-Category Classification Scheme (n = 188) Nursing Intervention I. Physical assessment 2. Give meds: IM, oral, eye, rectal, IV, sublinguat, ear, subq, inhaler 3. Vital signs 4. Intake and output 5. Dressing change: incision 6. Check circulation, motion, sensation 7. Foley catheter care 8. Up in chair/commode/wheelchair 9. Provide hygiene care (bath, teeth, perineal care) t0. Maintain IV; IVfluids 11. Catheterization; insert Foley catheter, recatheterize 12. Insert IV; restart IV 13. Measure and maintain elastic antiembolic stockings 14. Evaluate effectiveness of pain-relief measures 15. Discharge planning; plan for discharge 16. Cough, deep breathe 17. Provide pain relief measures before activity 18. Spirometry 19. Emotional support and encouragement 20. Position for comfort 21. Obtain operating room permit 22. Reposition/turn (to prevent complications) 23. Blood/blood product administration 24. Buck's traction 25. Assess pain 26. Discontinue IV/heperin lock 27. Ice packs 28. Discontinue Foley catheter 29. Skin care (clean and dry) 30. Set up/maintain oxygen, per nasal cannula/mask 31. Preoperative teaching 32. Set mutual goals 33. Discontinue/remove wound drain 34. Communication with family 35. Assist with oral intake 36. Assess drain 37. Massage

Intervention Category

t. Monitoring 6. Medications 1. Monitoring 15. Nutrition/fluid 5. Treatments 1. Monitoring 5. Treatments 2. Activities 2. Activities 15. Nutrition/fluid 7. Invasive insertions 7. Invasive insertions 5. Treatments 1. Monitoring 10. Coordination 4. Airway maintenance 3. Comfort 4. Airway maintenance 8. Emotional support 3. Comfort 12. Assisting other 11. Protection 15. Nutrition/fluid 3. Comfort 1. Monitoring 5. Treatments 3. Comfort 5. Treatments 11. Protection 4. Airway maintenance 9. Teaching 10. Coordination 5. Treatments 10. Coordination 15. Nutrition/fluid 1. Monitoring 3. Comfort

No. (%)*

205 (109.0) 196 (104.3) 179 (95.2) 175 (93.1) 165 (87.8) 144 (76.6) 140 (74.5) 139 (73.9) 132 (70.2) 131 (69.7) 116 (61.7) 113 (60.1) 112 (59.6) 110 (58.5) 103 (54.8) 100 (53.2) 97 (51.6) 97 (51.6) 95 (50.5) 89 (47.3) 86 (45.7) 81 (43.1) 81 (43.1) 79 (42.0) 78 (41.5) 78 (41.5) 72 (38.3) 72 (38.3) 72 (38.3) 66 (35.1) 66 (35.1) 66 (35.1) 64 (34.0) 62 (33.0) 58 (30.9) 57 (30.3) 57 (30.3)

Abbreviations: IM, intramuscular; IV, intravenous; subq, subcutaneous. *The same type of intervention may have been with more than one nursing diagnosis; includes interventions associated with and not associated with a nursing diagnosis; if asociated with a diagnosis, it was net listed with unassociated nursing interventions. Data for intervention category from Werley and Lang (1988).

SD STATEWIDE NURSING MINIMUM DATA SET PROJECT

mode, 3.5; SD = 1.0). The initial record reviewed at each site was more time-consuming than the last. Recording the nursing interventions--the most timeconsuming item on the tool--took a mean of 0.8 hours (SD = 0.6; range, 0.2 to 3.5). The median was 0.7 hours. Nursing diagnoses and interventions were coded for computer data entry. The mean coding time was 0.5 hours (SD = 0.4; range, 0.05 to 2.8; median, 0.4). Computer data entry into Excel took 48 hours for all 188 records, which included verifying the data entered and correcting errors. Discussion

NMDS data were available and able to be collected. Use of the NMDS provided a description of nursing practice and enabled comparisons among facilities and patient populations. Pain and impaired mobility, the most frequent NANDA diagnoses, were identical to those found for patients with a medical diagnosis of open reduction fixation tibia/tibia (Ryan et al., 1994). In that study, the frequencies of those nursing diagnoses were much lower than the combined categories in this study: 56 per cent compared with 89.9 per cent for pain and 27 per cent compared with 59.6 per cent for impaired mobility. Many (36.4 per cent) of the NANDA diagnoses in this statewide study were unresolved. However, 158 (84.0 per cent) patients were transferred; unresolved diagnoses on transfer may have been referred. The high number of transfers may have been related to the age of the patients (80.9 per cent were older than 70 years). O f the 70 patients with a length of stay longer than the mean of 8.3 days, 57 patients were transferred to either a long-term care facility, swing bed, or rehabilitation. Among these 57 patients, there were a total of 257 nursing diagnoses, with a mean of 4.5 nursing diagnoses (higher than the 4.0 mean for the entire sample). At discharge, 24 of these 57 patients had a total of 98 nursing diagnoses; 20 patients' nursing diagnoses were entirely resolved (3 patients had no nursing diagnoses; the remaining 10 patients had 1 or more diagnoses without outcome data). However, 15 patients from the same facility had 77 nursing diagnoses that were unresolved. The mean length of stay for these 24 patients was 12 days, which may be related to waiting for an available transfer bed. The most frequent nursing interventions are somewhat predictable in that the tasks listed are common technical nursing skills no matter what kind of patient (assessing, medicating, vital signs, intake and output, etc). Some interventions recorded for several patients

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at one facility but rarely or never recorded for patients from other facilities included towel roll under heels bilaterally (13.8 per cent), report to charge nurse (13.3 per cent), and identify with red dot (11.7 per cent). It is interesting, however, that there was some inconsistency in the frequency of related interventions, most notably with the Foley catheter. Foley catheter care (74.5 per cent) was recorded more frequently than catheterization (61.7 per cent) and was present almost twice as often as discontinue Foley catheter (38.3 per cent). Perhaps these patients were admitted and/or discharged with the Foley catheter. Some differences could be explained by certain activities being considered a standard of care. Two examples are bed in low position, which was on only 36 (19.1 per cent) medical records, and nothing by mouth, which was on only 32 (17.0 per cent) records. Other interventions may not be specifically recorded if the activity was obviously done by other evidence in the record, such as obtaining an operating permit, which was recorded on only 86 (45.7 per cent) medical records.

The data emphasize the value nursing has placed on documenting technical skills.

Nursing interventions denoting communication and facilitation skills were less frequently documented overall than technical tasks. Four interpersonal nursing interventions were on at least 30 per cent of the medical records: emotional support and encouragement (n = 95; 50.5 per cent), preoperative teaching (n = 66; 35.1 per cent), setting mutual goals (n = 66; 35.1 per cent), and communication with family (n = 62; 33.0 per cent). Only two nursing interventions relating to coordination of care were on at least 30 per cent of the medical records: discharge planning was on 103 (54.8 per cent), and setting mutual goals was on 66 (33.0 per cent). Nursing intensity collected in 24-hour periods can be trended. Standardizing the intensity for comparison would be preferable but cannot be done retrospectively. Comparisons among facilities can really only be done with the same care mix models. Data collection required 310.8 hours. With an hourly wage of $15.00, the cost would be $24.80 per record (excluding overtime and benefits). This cost was similar to a study with an average actual cost of $20.20 (Delaney, Mehmert, Prophet, & Crossley,

82

KARPIUK, DELANEY,AND RYAN

1994). Recording nursing interventions took 48.2 per cent of the total time. It took an average of 60 hours or 2.4 hours/record to collect, code, and enter data for 25 medical records. Two factors could have reduced the data collection time. First, even in facilities that had UHDDS available on a computer, the departments of data processing/medical records were reluctant to print the UHDDS for the selected patient records when the record was to be reviewed by the data collector; time and resources were the factors cited. The second factor was staffing intensity and staff mix, which are valuable data for reimbursement considerations but were difficult to retrieve in the system. A few facilities had some intensity and staff mix information computerized. Using data from a few specific records rather than all records for a time period required manual calculations. Conclusion

The data emphasize the value nursing has placed on documenting technical skills. Equally important are communication and facilitation activities. Nurses need to document all pertinent nursing interventions to give a complete picture of nurses' expertise when caring for patients. The coordination of care done by nurses should be clearly evident in the patient record. Manual data collection costs would be prohibitive on a larger scale. Hospitals are gradually moving to computerized nursing documentation systems. Nurse executives must be aware of the effect that decisions about computer technology can have on developing data bases. They need to know the types of software that will facilitate data base development. If software is purchased with data base development as a priority,

perhaps establishing a broad and comprehensive data base will progress more quickly. Possible reasons for the slow progress are lack of knowledge about the NMDS, lack of resources to collect and analyze data, and lack of vision to understand the effect the information could have on health policy. If hospitals choose nursing information systems structured to include the NMDS elements, it will facilitate analysis of larger data sets. Manual data collection without adequate resources to complete the project is not recommended. Because these data were from a rural state, this study may help rural hospitals justify the urban/rural classification category for Medicare. These data will also be compared with NMDS data from other sites. This study supports national efforts toward establishing an NMDS offering numerous benefits to nurses, including enhanced documentation of care, identification of trends related to patient care problems and nursing care needed, improved costing of nursing services and measurement of resources consumed, continued development and refinement of nursing information systems and decision making, and advancing nursing as a research-based discipline.

Acknowledgment The authors thank the data collectors: Colette Brust, RN, Director of Nursing, and Deb Simdorn, RN, St. Luke's Midland Regional Medical Center, Aberdeen; Sandy Holleman, RN, Primary Nurse, Sioux Valley Hospital, Sioux Falls; Vicki Munson, RN, Director of Orthopedics, McKennan Hospital, Sioux Falls; Loretta Nuttle, RN, Rapid City Regional Hospital, Rapid City; Sylvia Pickard, RN, Brookings Hospital, Brookings; Michelle Sachtjen, RN, Infection Control Officer, St. Mary's Hospital, Pierre; Karen Schneider, RN, Queen of Peace Hospital, Mitchell; Colleen Smith, RN, St. Michael's Hospital, Tyndall. Computer data entry was capably provided by Deloris Wynia, Attestation Coordinator, Sioux Valley Hospital, Sioux Falls, SD.

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SD STATEWIDE NURSING MINIMUM DATA SET PROJECT

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