Applying the Chronic Care Model to Improve Care and Outcomes at a Pediatric Medical Center

The Joint Commission Journal on Quality and Patient Safety 2017; 43:101–112

Applying the Chronic Care Model to Improve Care and Outcomes at a Pediatric Medical Center Jennifer Lail, MD, FAAP; Pamela J. Schoettker, MS; Denise L. White, PhD, MBA; Bhavna Mehta, MBA (MIS); Uma R. Kotagal, MBBS, MSc

Background: Cincinnati Children’s Hospital Medical Center launched the Condition Outcomes Improvement Initiative in 2012 to help disease-based teams use the principles of improvement science to implement components of the Chronic Care Model and improve outpatient care delivery for populations of children with chronic and complex conditions. The goal was to improve outcomes by 20% from baseline. Methods: Initiative activities included review of the evidence to choose and measure outcomes, development of conditionspecific patient registries and tools for data collection, patient stratification, planning and coordinating care before and after visits, and self-management support. Results: Eighteen condition teams, in sequenced cohorts, fully participated in the three-year initiative. As of October 1, 2015, data from 27,221 active patients with chronic conditions were entered into registries within the electronic health record and being used to inform quality improvement and population management. Overall, 13,601 of these children had an improved outcome. Seven of the teams had implemented their evidence-based interventions with ≥ 90% reliability, 83% of teams were regularly using an electronic template to plan care for a child’s condition before an encounter, 89% had stratified their population by severity of medical/psychosocial needs, 56% were using registry care gap data for population management, and 72% were doing self-management assessments. Eleven teams achieved the numeric goal of 20% improvement in their chosen outcome. Conclusion: The results suggest that, by implementing quality improvement methods with multidisciplinary support, clinical teams can manage chronic condition populations and improve clinical, functional, and patient-reported outcomes. This work continues to be spread across the institution.

I

t has been estimated that approximately 20% of children in the United States suffer from a chronic condition1,2 or have a special health care need.3 Yet, it has been reported that they receive only half of the care recommended.4 Often the care is episodic, oriented toward acute events, and does not include the longer-term, longitudinal elements critical to achieving better outcomes. Hospitalizations for children with chronic and complex conditions are consuming an increasing portion of inpatient care and resources.5–7 To optimize clinical and functional outcomes, these children also require outpatient, specialty-based follow-up after discharge. Improving the health care system’s ability to deliver effective, evidence-based chronic-condition care in the outpatient setting is becoming increasingly important. The Chronic Care Model was developed in the mid1990s to guide providers in delivering patient-centered and evidence-based care in order to improve outcomes for patients with chronic illness.8 The goal is to enable more productive interactions between informed and activated patients and a prepared and proactive health care team.9–13 Use 1553-7250/$-see front matter © 2016 The Joint Commission. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jcjq.2016.12.002

of the Chronic Care Model has been shown to lead to improvements in care processes and quality for patients with chronic illnesses, although there is often a delay in seeing improvements in clinical outcomes.14 The 2010–2015 strategic plan for Cincinnati Children’s Hospital Medical Center (CCHMC) set a goal of measuring and improving clinical outcomes for children with chronic conditions through population management, delivery system improvements for reliable application of evidence-based care, and coordinated, planned care with self-management support for children, youth, and families. Although foundational work had been done for patients with cystic fibrosis and inflammatory bowel disease,15–20 gaps remained between the care we were providing and the best care we could provide. Teams providing care for children with chronic and complex conditions needed enhanced support and training to optimally manage their clinical populations. Therefore, in 2012, the Condition Outcomes Improvement (COI) Initiative was launched to accelerate the pace at which disease-based teams could, using the principles of improvement science, implement components of the Chronic Care Model and standardized care delivery while identifying and closing gaps in care. We report here on the details and results of the COI Initiative.

102

Jennifer Lail, MD, FAAP, et al

METHODS Setting

CCHMC is a large, urban pediatric academic medical center that has maintained a significant focus on improving evidencebased and family-centered care since the late 1990s.21 Questions from the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture are included on the survey administered to all employees every two years. Quality and safety are promoted and rewarded in all aspects of patient care. The James M. Anderson Center for Health Systems Excellence (Anderson Center) at CCHMC facilitates and supports a wide range of improvement initiatives. In fiscal year 2015 CCHMC had more than 33,000 inpatient admissions, 100,000 emergency department visits, and 916,000 specialty outpatient visits, and performed more than 32,000 surgical procedures at the main campus and 10 neighborhood locations. Approximately 44% of patients have Medicaid insurance. In early 2007 CCHMC began a phased installation of enterprise electronic health record (EHR) software from the Epic Systems Corporation. Planning the Initiative

Between the fall of 2012 and the fall of 2014, 17 pediatric and surgical divisions were invited to participate in the COI Initiative. The global aim was to improve outcomes for patients and families with chronic and complex conditions. Twenty-nine existing chronic condition improvement teams were identified for participation. The specific aim was 20% improvement in each team’s chosen outcome. Eighteen improvement teams participated fully in the initiative (Table 1). The data reported here for those 18 teams were collected from January 2013 through September 2015 (the end of the first quarter of fiscal year 2016). Five of the remaining 11 invited teams participated in the initiative but had difficulty applying the Chronic Care Model because the symptoms were defined by multiple possible etiologies (examples include fainting and pain as key diagnostic mechanisms). These teams did not collect data after June 2015. Two others never fully engaged, and 4 started the initiative more recently and continue to establish their baselines and collect data. The COI teams consisted of physicians, nurses, care managers, family members, mental health providers, and social workers. CCHMC provided funding and access to staff from Information Services, Patient Services, and the Anderson Center with expertise in information technology and the EHR, quality improvement (QI), evidence and measurement, and data analytics. Each clinical team worked from a sequenced curriculum (summarized in Appendix S1, available in online article) to define and stratify the population of interest, build a population registry in the EHR, select key evidence-based outcomes and measurement strategies, and develop clinical tools and

Chronic Care Model Improves Pediatric Outcomes

processes to support the team’s improvement work. All but one of the participating divisions had prior QI training. The condition teams dedicated two hours each week for eight to nine months to these tasks. They were guided by the They were guided by the Team Roadmap for Improving Care for Children and Adolescents with a Chronic Condition32 and supported by a multidisciplinary implementation team consisting of a QI consultant and data analyst affiliated with their division, an Epic analyst from Information Services, a clinical practice consultant from Patient Services (Nursing and Allied Health), volunteer parent advisors, and internal experts in evidence, measures, and chronic care. QI, data analytic and support leaders from multiple departments provided oversight, monitored progress toward goals, and helped overcome barriers. Each clinical team observed and mapped processes to decrease variation and increase reliability in care processes. A key driver diagram template (based on evidence from the literature, observed opportunities for improvement, and available baseline data) was available to help teams prioritize their interventions (Figure 1). Improvement experts from the Anderson Center guided teams’ improvement efforts using failure mode and effects analysis,33 Plan-Do-Study-Act cycles,34 and Pareto charts of failures.35 The Anderson Center also delivered weekly provider-specific feedback on team performance, and documentation of the key clinical processes guided teams to test actions to remediate suboptimal performance. Provider and team-level feedback on the overall outcome, process, and patient-reported outcome measures was provided monthly in the form of run and control charts.36,37 Forty-two parents of children with one or more of the study conditions served as vital partners and engaged members of clinical teams. Families were physically present at team meetings and further participated in conference calls, electronic surveys, focus groups, and electronic communication of parent input. Participating board-certified pediatricians were eligible for Part 4 Maintenance of Certification credit from the American Board of Pediatrics.38 Improvement Activities

Improvement activities focused on five goals: identifying the target populations, selecting and measuring outcomes and supporting processes, establishing pre-visit planning (PVP), building and implementing care coordination, and assessing and addressing self-management support. The teams were free to choose the interventions that they thought would work best for their patient population. Implementation details are provided for the team focused on juvenile idiopathic arthritis (JIA). Review of Evidence to Choose and Measure Clinical Outcomes. With support from evidence appraisers in the Anderson Center, clinical teams evaluated existing literature to select outcomes for measurement and identify best practices to improve each outcome measure. If no existing evidence was available, the teams developed best practices

Volume 43, No. 3, March 2017

103

Table 1. The 18 Condition Teams That Fully Participated in the Three-Year Condition Outcomes Improvement Initiative Condition

Active Registry Population

Outcome Measure

Process Measure % patients for whom key elements of care were documented every visit: asthma severity, documentation of action plan, patient’s perception of asthma control, pulmonary function test in the past 13 months, flu vaccine received % visits with Asthma Control Test score documented at each visit

Asthma—General Pediatrics

5,715

% patients 2–17 years of age whose asthma was well controlled: most recent Asthma Control Test22,* score ≥ 20

Asthma—Pulmonary

1,390

Cardiomyopathy

1,295

% patients 4–20 years of age whose asthma was well controlled: most recent Asthma Control Test score ≥ 20 % patients whose symptoms improved since their initial visit, as measured by the Pediatric Quality of Life Inventory (PedsQL)23 % patients with systolic blood pressure below target % patients in bowel management program with improved fecal incontinence from baseline to day 14 % patients with previously documented food allergies resolved % patients with non-intractable epilepsy who report seizure freedom for 12 months

Chronic kidney disease Fecal incontinence

295 571

Food allergy

2,883

General epilepsy

3,069

New onset epilepsy

933

Hemophilia

880

Juvenile idiopathic arthritis Kidney transplant

775 98

Major depressive disorder

1,381

Pathologic aggression in attention deficit/hyperactivity disorder Menorrhagia

3,029

673

Posterior urethral valves

183

Sickle cell disease

417

Systemic lupus erythematosus

130

Torticollis

3,504

*References are found on page 111.

% decrease in new onset epilepsy patients that remain below the unhealthy range: < 65.4 on the PedsQL % patients with severe hemophilia A and hemophilia B ≥ 3 years of age without inhibitor who have < 3 joint bleeds per year % patients with inactive disease: American College of Rheumatology criteria26 % patients with systolic blood pressure controlled % patients who experience functional improvement, as measured by the Global Assessment of Functioning scale27 % patients with an improved Clinical Global Impressions scale28

% patients with decrease in menorrhagia assessment tool score (adapted from Philipp et al.29). % nontransplant patients with stable renal function: no two-stage increase in the chronic kidney disease staging30 since initial staging % patients whose fetal hemoglobin is ≥ 20% % patients with controlled disease: Systemic Lupus Erythematosus Disease Activity Index31 score < 5 % patients who achieved complete resolution within 6 months of initial visit

% patients whose symptoms improved since their initial visit, as measured by the PedsQL23

% patients with correctly measured and documented blood pressure recorded % completion of locally developed fecal incontinence Social Continence Survey by day 14 % patient charts with completed exit flow sheet on reevaluation of allergy status % completion of patient/family surveys (PedsQL, Pediatric Epilepsy Side Effects Questionnaire, and Seizure Frequency)23–25 at or above 90% % completion of patient/family surveys (PedsQL, Pediatric Epilepsy Side Effects Questionnaire, and Seizure Frequency)23–25 at or above 90% % severe patients on primary prophylactic factor replacement by age 3 years % patients with American College of Rheumatology score recorded % patients with correctly measured and documented blood pressure recorded % completion of Global Assessment of Functioning scale and suicide screening at every visit % completion of Clinical Global Impressions scale28 and suicide screening at every visit

% visits in which menorrhagia assessment tool was collected at initial and follow-up visits % patients for whom renal staging assessment was performed

% patients ≤5 years of age started on or offered hydroxyurea % patients with Systemic Lupus Erythematosus Disease Activity Index score completed at each visit % patients receiving 5 components of treatment plan: neck passive range of motion, neck and trunk active range of motion, symmetrical development of movement, environmental adaptations, parent/caregiver education

104

Jennifer Lail, MD, FAAP, et al

Chronic Care Model Improves Pediatric Outcomes

Key Driver Diagram Template That Teams Used to Prioritize Their Interventions

Figure 1: A key driver diagram template was available to help guide and prioritize team interventions. The template included SMART (specific, measurable, achievable, relevant, and time-bound) and global aims, addressed the theory or hypothesis driving the aim, and listed interventions necessary to achieve the aims.

through consensus. The JIA team selected the stringent national criteria for inactive disease developed by the American College of Rheumatology.26 The teams also identified key supporting processes that had to be accomplished reliably to improve the chosen outcome. The defined and validated clinical outcome measures fell into one of four primary categories: disease remission, disease control, patient-reported outcomes (PROs), and symptom management (Table 2). Two teams chose to target a PRO for improvement. To integrate data collection into clinic work flow, validated questionnaires for PROs were embedded in the EHR and on electronic data collection tablets for completion by patients and families. The selected questionnaires measured health-related quality of life, disease-specific physical function, and psychological functioning. Development of Electronic Health Record Registries and Tools. Developing an electronic population base helped teams know which patients were being seen for care, which were missing care or needed additional services, and how their patients were doing clinically.

Clinical teams defined their patient populations and developed criteria for inclusion in patient registries embedded within the EHR. The inclusion criteria chosen by the JIA team were patients with a JIA diagnosis and at least one inperson visit to a CCHMC JIA clinic in the past three years. Curation of the registry was a combination of manual and automated processes. Using EHR diagnosis coding groupers and patient lists, teams identified patients for inclusion in their population registry, then validated the registry to define their active subpopulations based on patients seen within a specific time frame. Electronic modifiers were applied to identify inactive patients. Each team identified both an individual and a process to maintain registry accuracy over time regarding new patients, patients who have died, and patients who have left the practice. On the JIA team, an advanced practice nurse was responsible for maintaining the registry. While new patients were added automatically based on International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, she removed inappropriate or deceased patients and reviewed automated reports. Electronic tools to support practice transformation, such as risk-stratification tools, PVP templates, and

Volume 43, No. 3, March 2017

105

Table 2. Identified Outcome Measure Categories Type of Outcome Measure

Description

Disease remission outcomes

Measures the percentage of the active population who met the criteria for disease remission, as defined by existing evidence—based on results captured during the last encounter. Measured as a population. Measures the percentage of active patients who met the criteria for having maintained or improved disease control based on available evidence. Can be measured as a population-based measure or encounter-based measure.

Number of patients in remission/active population

• Food allergy • Juvenile idiopathic arthritis • Torticollis

Number of patients with disease control/number of eligible patients

• • • • • • • • •

Measures the percentage of active patients who report quality of life scores that identify them as in a healthy range. Can be used as a population- or encounterbased measure and also measured negatively (e.g., percentage of patients reporting unhealthy quality of life) Measures the percentage of patients who are able to manage their symptoms and keep them under control, as defined by evidence. Measure could be encounter- or population-based and would typically be used in cases in which remission is not viable.

Number of patients in healthy range/number of eligible patients

Disease control outcomes

Patient-reported outcomes—quality of life

Symptom management

self-management assessments, were developed in the EHR. Seven standard care gap reports were built to identify missed care, needed services, or unanticipated health care use. The care gaps varied by team and condition and included patient needs, resource use beyond the clinic (emergency department visits, hospital admissions), patients requiring followup, failure to keep appointments, self-management issues, unmet psychosocial needs, and the caseloads of care managers and social workers. Data infrastructure and reporting systems were created to support measure development, analytics, and enhanced care gap reports from the EHR data. Patient Stratification. To identify patients with the highest risk for unmet needs, medical and psychosocial complexity were assessed to match an appropriate level of care to each patient’s need. To triage for a population needs

Calculation

Number of patients with symptoms under control/ number of eligible patients

Teams Using This Measure

Asthma—General pediatrics Asthma—Pulmonary Chronic kidney disease General epilepsy Hemophilia Kidney transplant Posterior urethral valves Sickle cell disease Systemic lupus erythematosus • Cardiomyopathy • New onset epilepsy

• Fecal incontinence • Major depressive disorder • Pathological aggression in attention deficit/hyperactivity disorder • Menorrhagia

assessment, two teams that addressed large population conditions (epilepsy, cardiomyopathy) used the Pediatric Medical Complexity Algorithm for an initial medical stratification.39 All clinical teams, including epilepsy and cardiomyopathy, used a locally developed matrix to stratify their patients into one of three levels of clinical medical complexity (stable health status, variable health status, and declining health status) and one of three levels of social complexity (prepared proactive patient/family; intermittent barriers to care and/or significant adherence issues; and complex, persistent barriers to care and/or serious child/family safety issues). Teams that considered all of their patients to be at equal medical risk did not apply risk stratification. Although care gap reports and self-management assessments were adaptable to all conditions, some teams chose to defer their use until they had the capacity to do this level of population management. The JIA

106

Jennifer Lail, MD, FAAP, et al

team did further sub-stratification, using its internally developed decision support tools, to focus on children who had not achieved disease inactivity. Planning and Coordinating Care Before, During, and After Visits. Teams examined the flow of ambulatory patients through a clinic visit and the tasks performed by each team member in order to streamline processes, eliminate duplicative work, and reallocate work to the most appropriate team member.40 Registered nurses and care managers led care coordination activities for medically highrisk patients. Social workers managed and directed activities to mitigate psychosocial risk. Together, they integrated the plan of care and provided support for patients and families. Supported by their population registries, teams developed a PVP note and process to prepare for upcoming ambulatory encounters. For children stratified at the highest level of medical complexity, processes were standardized to identify interim health care encounters, clinical services/ referrals needed, and a child’s indicated lab/imaging needs in advance of a clinical visit. Some teams obtained parent input prior to the visit via phone calls by clinic staff, questionnaires on paper and electronic tablets, and, in some cases, use of an EHR patient portal. The JIA team has parents actively advising the team at local and national levels through the Pediatric Rheumatology Care and Outcomes Improvement Network.41 The PVP note, embedded into the EHR, summarized care coordination needs, required interventions, and selfmanagement or adherence issues. Nurses, social workers, and providers used the PVP note to recommend care to support the measured outcomes and related processes, and to pend orders for the upcoming visit and follow-up care. Each team defined its population needing PVP and developed a reliable method to ensure review of the planning note before a patient visit. Each condition team also mapped its staff assignments and work flows for care support before, during, and after the visit. The team’s PVP helped to identify and prioritize needed care and helped the team work together with families to complete needed labs, imaging, and referrals and to access resources. An experienced registered nurse used the JIA PVP prior to every patient visit. This nurse applied the team’s decision support rules to determine which visits required more attention to therapeutic regimen, adherence issues, psychosocial support, missed care, or follow-up. Physicians, nurses, and medical assistants then planned ahead for the visit, pending orders for needed care (labs, referrals, vaccines, imaging) and gathering pertinent health information from outside of CCHMC. Self-Management Support. Staff from each clinical team participated in self-management support skills training and developed their clinic work-flow processes to assess patient/ family need for support. A self-management assessment tool in the EHR was used to assess patient/family confidence in

Chronic Care Model Improves Pediatric Outcomes

managing care at home, their level of worry, their concerns, and their need for support. Some teams partnered with their parent advisors to develop additional tools for use in clinic visits to prompt families to ask more questions and discuss concerns. The JIA team concentrated on patient goals for the visit, self-efficacy, distress/worry, and adherence, thus allowing resources to be focused on those with higher needs and complexity. The team also used a barriers checklist to allow patients or families to identify specific difficulties in managing care at home related to oral medications, injections, infusions, or occupational or physical therapy exercises. Study of the Initiative

Data Collection. Baseline data were collected by the condition teams as they built and tested their patient registry, tools, and processes. The study population was defined by chronic condition, with criteria for inclusion/exclusion based on a search of available evidence and concurrence from the team. In general, a broad population for a condition included all patients with the condition who were seen by any CCHMC provider within the previous 36 months. Each condition team then defined active subpopulations of patients for whom the team applied therapeutic interventions and collected data to measure improvement. Reported data on outcome and process measures are for the condition’s active subpopulation. Examples of subpopulations included disease categories (for example, new onset epilepsy vs. general epilepsy), geographic groups, age groups, and other relevant stratification appropriate for the condition. Reported data on care coordination, care-gap reporting, and self-management reports were for the entire active registry population. Measures. Teams identified the key interventions that would affect the overall outcome and tracked their reliability. The goal was ≥ 90% reliability, or ≤ 1 error per 10 opportunities.42 Examples of reliability measures included provision of education about the condition, accurate disease staging, completion of necessary lab tests, and completion and documentation of measurement tools in the EHR. Data Analysis. QI tools, including run charts and statistical process control (SPC) charts,36,37 were used to track changes in outcomes and process reliability over time. SPC methodology worked well for measuring improvement in outcomes for teams that were able to view their patients in cohorts or use visits/encounters. Following SPC rules, each goal was assessed based on the centerline of the outcome measure to account for variation and to ensure that the system was performing at the desired level. However, populationbased outcome measures that captured the percentage of a population with inactive or improved conditions did not comply with the assumptions associated with SPC. This was typically caused by autocorrelation in the outcome measure that was based on population results rather than independent observations. For such autocorrelated data, we compared results for the population measure at baseline to the results

Volume 43, No. 3, March 2017

during the last quarter of data collection and used time series modeling to identify outliers in the process.43 Goal achievement was assessed by doing a t-test comparison between the population results during the baseline period and the results of the last quarter. Approaching this using an interrupted time series may have been appropriate if we were not engaged in active QI work, but teams used multiple tests that would have caused several interruptions in the time series and likely confounded the results. For this report, a comparison of the baseline to current performance was graphed using a bar chart. To estimate the percent improvement in outcome measures above that expected had the interventions not been initiated, we assumed that the process would have remained consistent and produced the same results if the interventions had not been implemented.

107

registries for these conditions. Eleven of the 18 fully participating condition teams (61%) achieved the goal of 20% improvement in their chosen clinical outcome (Figure 2). The posterior urethral valves team began with the goal of maintaining stable renal function. However, because the team was not doing any renal staging assessment at that time, team members did not have the information required to define an achievable goal. The team selected prevention of an increase of two chronic kidney disease stages as the goal but struggled with reliable measurement and documentation of renal staging. After receiving its data, the team chose to continue with this nonaggressive goal while working on process reliability of renal staging assessment. Two of the 18 teams showed lesser but measurable improvement, and the remaining 5 either maintained or showed a decline in their baseline performance.

Human Subjects Protection

A description of the initiative was submitted to the CCHMC Institutional Review Board, which determined that it did not constitute human subjects research. RESULTS Patients and Teams

As of October 1, 2015, data from 27,221 active patients with chronic conditions were entered into EHR–embedded patient

Evidence-Based Interventions

Seven of the teams implemented their evidence-based interventions with ≥ 90% reliability. Teams were guided by regular review of run charts for outcome improvement and reliability of their key process measures. Appendix S2 (available in online article) shows a sample run chart for a JIA outcome measure and a control chart for a JIA process measure. Fifteen condition teams (83%) regularly used an

Primary Outcome Measures for Each Condition at Baseline and the End of the First Quarter (Q1) of Fiscal Year 2016

Figure 2: The specific aim was 20% improvement in each team’s chosen outcome, which 11 of the 18 fully participating condition teams achieved. Two teams showed lesser but measurable improvement, and the remaining 5 teams either maintained or showed a decline in their baseline performance. ADHD, attention deficit/hyperactivity disorder.

108

Jennifer Lail, MD, FAAP, et al

Chronic Care Model Improves Pediatric Outcomes

Table 3. Use of Elements of the Chronic Care Model Condition

Registry

Care Gap Reports

Self- Management Assessment

No

No

Yes

No

Yes

Yes

Yes Yes Yes Yes, tier 2 and 3 follow-ups Yes, tier 2 and 3 follow-ups Yes, all comprehensive clinic visits, all tiers Yes Yes No No

Yes No Yes No No Yes

Yes No No Yes Yes Yes

Yes Yes No No

Yes Yes Yes Yes

Risk Stratification

Pre-visit Planning

Asthma—General Pediatrics Asthma—Pulmonary

Yes

Yes

Yes

Yes

Cardiomyopathy

Yes

Chronic kidney disease Fecal incontinence Food allergy General epilepsy New onset epilepsy Hemophilia

Yes Yes Yes Yes Yes Yes

Yes, Pediatric Medical Complexity Algorithm (PMCA)* Yes Yes No Yes, PMCA Yes, PMCA Yes

Juvenile idiopathic arthritis Kidney transplant Major depressive disorder Pathologic aggression in ADHD Menorrhagia Posterior urethral valves Sickle cell disease

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes

Yes Yes Yes, all nonurgent patient visits Yes

Yes Yes Yes

Yes Yes No

Yes Yes Yes

Yes

Yes

Yes

No, but screened for comorbidities

No

No

No

Systemic lupus erythematosus Torticollis

Yes Yes

Yes, every patient, every visit All new visits and all asthma follow-up appointments Yes

*See reference 39 on page 111. ADHD, attention deficit/hyperactivity disorder.

electronic template to plan care for a child’s condition before an encounter (Table 3). Sixteen teams (89%) stratified their population by severity of medical/psychosocial needs. Ten teams (56%) used registry care gap data for population management, and 13 teams (72%) did self-management assessments. During the initiative, 2 symptom-based teams redirected their active QI work. After the syncope team better understood its patient population, the team chose to switch to a more relevant outcome around value by conducting phone follow-ups instead of face-to-face visits in the lowrisk population sector. After achieving its their first goal, the menorrhagia team began to address a second outcome aimed at preserving fertility in oncology patients. Patient Outcomes

Overall, 13,601 of the children included in the active registry population (50%) had the desired or an improved outcome. Table 4 estimates the percent improvement in outcome measures above that expected had the interventions not been initiated for all 18 conditions (Table 4a) and

for just the conditions that showed improvement (Table 4b). Twelve percent more children in the active registry population achieved the desired outcome as a result of the effort. The three teams that worked toward outcomes in disease remission saw the largest percent increase in patients achieving the desired outcome, 25%. The nine teams that pursued disease control saw the smallest improvement in the patients achieving the desired outcome, 3%. For the two teams that targeted a PRO for improvement, 21% more patients met the desired outcome. The four teams focused on symptom management saw a 20% improvement in the number of patients achieving their goal. DISCUSSION

Our results suggest that, by implementing QI methods and with multidisciplinary support, clinical teams can manage diverse populations with chronic conditions and improve their outcomes. Our strategy was the delivery and measurement of the right care to each patient, data feedback to clinical teams, and planned, deliberate population management strategies for groups of children/youth with a specific chronic

Volume 43, No. 3, March 2017

109

Table 4. Outcome Measure Results

Outcome Measure a. Results for All 18 Conditions Disease remission Disease control Quality of life Symptom management All outcomes b. Results for the Conditions That Showed Improvement Disease remission Disease control Quality of life Symptom management All outcomes

No. of Children with an Improved Outcome

No. Expected to Have Improved Had the Interventions Not Been Implemented

3,032 6,454 1,398 2,717 13,601

2,434 6,257 1,152 2,260 12,103

2,538 5,074 894 1,865 10,371

1,802 4,815 648 1,382 8,647

condition, while identifying and addressing the family’s selfmanagement needs. Eleven of the 18 chronic condition teams in the COI Initiative achieved the goal of 20% improvement in their chosen clinical outcome. Two other teams showed smaller, but measurable, improvement by October 2015. Five teams were unable to demonstrate or sustain improvement. Eleven other teams originally invited to participate completed the measure selection, electronic registry build, and training around care coordination and self-management, but had difficulty applying the data collection and care transformation reliably in their clinical settings. Those teams cited time for QI work, staff turnover, leadership changes, and competing clinical and research priorities as barriers to implementation of all the components of the COI Initiative. Generation of relative value units (RVUs) in clinical care was a specific barrier to time dedicated to QI. No new full-time employees were added in order to implement this initiative. Each condition team leveraged new work to its existing staff. Teams whose division director was committed to standardization and accountability were more able to sustain the work. Teams with prior QI education or experience understood the work more easily. Successful teams also had a physician champion, motivation to change, and alignment with CCHMC’s strategic plan metrics. Prior experience working together, as well as dedication to process reliability, accountability for data collection, commitment to reliable application of evidence-based care, and adequate support staff to apply care coordination and self-management practice, helped make these teams more successful. Clinical teams with broad representation from their providers, nurses, families, social workers, and mental and nutritional health providers found it easier to select meaningful, measurable outcomes and identify the supporting key processes. Parents effected change in areas such as measurement selection, selfmanagement tools, clinic processes, and communication

% Improvement Greater than Expected Had the Interventions Not Been Implemented 25 3 21% 20 12

41 5 38 35 20

strategies. The family perspective kept teams focused on meaningful goals via team meetings, focus groups, and review of patient education materials. Successful teams also used the data completion reports, provider/team-specific feedback, and coaching from QI consultants to help drive results. A generic version of a PVP tool (which was developed based on the combined knowledge of what condition teams identified as important to consider before a visit) is now available to all CCHMC providers in our EHR (Appendix S3, available in online article). The JIA team has spread its specific PVP tool design and decision support rules nationally through the Pediatric Rheumatology Care and Outcomes Improvement Network. The JIA and other teams have instituted monthly population management sessions to close care gaps and for peer review of their most challenging cases to plan care. Although the improvement work was based in the outpatient clinic setting of CCHMC, care planning and coordination necessarily bridged, with the child, into inpatient and acute care settings. Condition-specific pediatric collaborative networks have used similar strategies across multiple institutions,44–48 but, to our knowledge, such clinical registry development, practice redesign, and self-management support have not been applied and measured in large populations of children with a broad range of chronic conditions within a single hospital system. Work with the 18 teams has taught us that outcomes need to be clinically meaningful and amenable to improvement. Management of a population defined by symptoms with multiple possible etiologies, such as syncope or menorrhagia, were less appropriate for the application of the Chronic Care Model elements. Leaders from Patient Services worked with teams to clarify roles and responsibilities and use each staff member’s skill to the maximal advantage in improving care, but changes in leadership and staffing challenged some teams, particularly if attrition occurred among condition champions.

110

Jennifer Lail, MD, FAAP, et al

Improvement requires dedicated time and bandwidth, and team leaders must commit to standardization and accountability. Although an initiative of this large scale may seem prohibitive in settings with less robust QI and information technology support, the concepts of population identification, stratification, outcome and process measurement, and planned, coordinated care can be applied in smaller settings. Indeed, these same concepts align with the Association of Maternal & Child Health Programs’ Standards for Systems of Care for Children and Youth with Special Health Care Needs.49 Even locations without access to an electronic registry can identify patient populations and stratify them according to their medical and psychosocial needs. Clinical review of a patient list can identify patients missing appointments or care, or those whose care is marked by emergency department visits, admission, or readmissions. Risk stratification of the patient, both medically and psychosocially, helps with appropriate deployment of care coordination and self-management supports and permits a simple process of PVP of care prior to an encounter. All CCHMC teams were encouraged to develop and test both tools and processes on paper prior to implementation in the EHR. In less resourced settings, such nonelectronic QI efforts can be applied. Several limitations may limit the generalizability of our results. We have reported results here only for the teams that were able to fully participate in the initiative. In support of this initiative, CCHMC provided funding and access to staff from Information Services, Patient Services, and the Anderson Center with expertise in information technology and the EHR, QI, evidence and measurement, and data analytics. The fact that our teams defined the active subpopulation for intervention as patients who had a clinical, conditionspecific encounter for care at CCHMC during a specified time frame limits the conclusions that can be drawn. Additional study is required on the children with these conditions who have not been seen for care. Additional limitations were that the treated patients were predominately children and that, except for general pediatricians treating asthma patients, participating physicians were specialists. This initiative began with a focus on population management, rather than raw numbers or results for individual patients. Twenty percent improvement was considered an inspirational goal. Over time, the similarities in the outcomes chosen became clear and we began to categorize the teams according to outcome type (disease remission, disease control, patient-reported/quality of life, and symptom management). Because each chosen measure was supported by an operational definition that specifies the criteria used for determination of inclusion and goal achievement, we chose to report the numbers of children in a population that showed outcome improvement in the four outcome categories. Our work to improve clinical outcomes at scale for children with chronic conditions continues. Four new teams have

Chronic Care Model Improves Pediatric Outcomes

started to collect data. Guided by defined, measurable population outcomes and key process measures, all teams continue to standardize patient care. Because timely and accurate data are key drivers for improvement, we are working to increase access to real-time outcome and process data to COI teams engaged in active improvement, with data attribution by provider and clinic. Teams have learned to use their own registry and care gap reports within the EHR, and 11 of the COI teams are developing new evidence-based algorithms for other aspects of their care. The teams that dropped out still have access to their registry and electronic tools. Some teams are using these for population management. However, they are not collecting data and driving QI. As of this time, there are no plans to engage them in a future improvement effort. Beginning in January 2016, a new care management model was implemented throughout CCHMC to identify those children at highest medical and psychosocial risk for intensive care management and self-management support and the staffing support needed for care coordination. These applications of the components of the Chronic Care Model, along with research and the implementation of new discovery, will continue to support improved outcomes for children with chronic conditions. CONCLUSION

With collaborative support for population management, outcome measurement, and implementation of components of the Chronic Care Model guided by QI methodologies and data analytics, clinical teams can improve outcomes for their pediatric patients with chronic conditions. Acknowledgments. The authors extend special thanks to all the COI teams for their collaboration, with particular gratitude to the Rheumatology Division at CCHMC for serving as an exemplary condition. This work and the article could not have existed without their dedication to outcomes improvement. Conflicts of Interest. All authors report no conflicts of interest.

Jennifer Lail, MD, FAAP, is Assistant Vice President, Chronic Care Systems, and Associate Professor of Clinical Pediatrics, Complex Care Center, James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center. Pamela J. Schoettker, MS, is Medical Writer, James M. Anderson Center for Health Systems Excellence. Denise L. White, PhD, MBA, formerly Assistant Professor/Director, Quality and Transformation Analytics, is Assistant Professor—Educator, Carl H. Lindner College of Business, University of Cincinnati. Bhavna Mehta, MBA (MIS), is Director, Quality and Transformation Systems Architecture, James M. Anderson Center for Health Systems Excellence. Uma R. Kotagal, MBBS, MSc, is Senior Vice President for Quality, Safety and Transformation, and Executive Director, James M. Anderson Center for Health Systems Excellence. Please address correspondence to Jennifer Lail, [email protected].

ONLINE-ONLY CONTENT See the online version of this article for Appendix 1. The Sequenced Curriculum Completed by Each Team. Appendix 2. A Run Chart for a JIA Outcome Measure and a Control Chart for a JIA Process Measure. Appendix 3. A

Volume 43, No. 3, March 2017

111

Generic Version of a PVP Tool Developed and Now Available to All CCHMC Providers in the Electronic Health Record.

21.

REFERENCES

22.

1. Van Cleave J, Gortmaker SL, Perrin JM. Dynamics of obesity and chronic health conditions among children and youth. JAMA. 2010 Feb 17;303:623–630. 2. van der Lee JH, et al. Definitions and measurement of chronic health conditions in childhood: a systematic review. JAMA. 2007 Jan 27;297:2741–2751. 3. Bethell CD, et al. What is the prevalence of children with special health care needs? Toward an understanding of variations in findings and methods across three national surveys. Matern Child Health J. 2008;12:1–14. 4. Mangione-Smith R, et al. The quality of ambulatory care delivered to children in the United States. N Engl J Med. 2007 Oct 11;357:1515–1523. 5. Berry JG, et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff (Millwood). 2014;33:2199–2206. 6. Perrin JM, Anderson LE, Van Cleave J. The rise in chronic conditions among infants, children, and youth can be met with continued health system innovations. Health Aff (Millwood). 2014;33:2099–2105. 7. Simon TD, et al. Children with complex chronic conditions in inpatient hospital settings in the United States. Pediatrics. 2010;126:647–655. 8. Improving Chronic Illness Care. The chronic care model. Model elements. Accessed Dec 14, 2016. http://www. improvingchroniccare.org/index.php?p=Model_Elements&s =18. 9. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA. 2002 Oct 9; 288:1775–1779. 10. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the Chronic Care Model, Part 2. JAMA. 2002 Oct 16;288:1909–1914. 11. Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1:2–4. 12. Wagner EH, et al. Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001;20:64–78. 13. Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Q. 1996;74:511–554. 14. Coleman K, et al. Evidence on the Chronic Care Model in the new millennium. Health Aff (Millwood). 2009;28:75–85. 15. Britto MT, et al. Improving influenza immunisation for high-risk children and adolescents. Qual Saf Health Care. 2007;16:363–368. 16. Crandall WV, et al. Improved outcomes in a quality improvement collaborative for pediatric inflammatory bowel disease. Pediatrics. 2012;129:e1030–e1041. 17. Ernst MM, et al. Using quality improvement science to implement a multidisciplinary behavioral intervention targeting pediatric inpatient airway clearance. J Pediatr Psychol. 2010;35:14–24. 18. McPhail GL, et al. Improving evidence-based care in cystic fibrosis through quality improvement. Arch Pediatr Adolesc Med. 2010;164:957–960. 19. Samson CM, et al. Improved outcomes with quality improvement interventions in pediatric inflammatory bowel disease. J Pediatr Gastroenterol Nutr. 2012;55:679–688. 20. Siracusa CM, et al. The impact of transforming healthcare delivery on cystic fibrosis outcomes: a decade of quality

23.

24.

25.

26.

27. 28.

29.

30.

31.

32.

33.

34.

35. 36. 37.

38. 39.

40.

41.

improvement at Cincinnati Children’s Hospital. BMJ Qual Saf. 2014;23(suppl 1):i56–i63. Britto MT, et al. Cincinnati Children’s Hospital Medical Center: transforming care for children and families. Jt Comm J Qual Patient Saf. 2006;32:541–548. Nathan RA, et al. Development of the Asthma Control Test: a survey for assessing asthma control. J Allergy Clin Immunol. 2004;113:59–65. Varni JW, Seid M, Rode CA. The PedsQL™: measurement model for the Pediatric Quality of Life Inventory. Med Care. 1999;37:126–139. Morita DA, Glauser TA, Modi AC. Development and validation of the Pediatric Epilepsy Side Effects Questionnaire. Neurology. 2012;79:1252–1258. Fountain NB, et al. Quality improvement in neurology: epilepsy Update Quality Measurement Set. Neurology. 2015;84:1483–1487. Wallace CA, et al. American College of Rheumatology provisional criteria for defining clinical inactive disease in select categories of juvenile idiopathic arthritis. Arthritis Care Res (Hoboken). 2011;63:929–936. Hall RCW. Global assessment of functioning. A modified scale. Psychosomatics. 1995;36:267–275. Guy W. Clinical global impressions. In: ECDEU Assessment Manual for Psychopharmacology, Rev. Rockville, MD: National Institute of Mental Health; 1976:218–222. Philipp CS, et al. Development of a screening tool for identifying women with menorrhagia for hemostatic evaluation. Am J Obstet Gynecol. 2008;198:163, e1–8. National Kidney Center. Chronic kidney disease: stages. Accessed Dec 14, 2016. http://www.nationalkidneycenter .org/chronic-kidney-disease/stages/. Bombardier C, et al. Derivation of the SLEDAI. A disease activity index for lupus patients. The Committee on Progress Studies in SLE. Arthritis Rheum. 1992;35:630–640. Cincinnati Children’s, James M. Anderson Center for Health Systems Excellence. A roadmap for teams treating patients with chronic conditions. Accessed Dec 14, 2016. http:// www.cincinnatichildrens.org/service/j/anderson-center/ chronic/roadmap/. Institute for Healthcare Improvement. Failure modes and effects analysis (FMEA) tool. 2004. Accessed Dec 14, 2016. http://www.ihi.org/resources/Pages/Tools/ FailureModesandEffectsAnalysisTool.aspx. Langley GJ, et al. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. San Francisco: Jossey-Bass, 1996. Tague NR. The Quality Toolbox. 2nd ed. Milwaukee: ASQ Quality Press, 2005. Amin SG. Control charts 101: a guide to health care applications. Qual Manag Health Care. 2001;9:1–27. Benneyan JC, Lloyd RC, Plsek PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care. 2003;12:458–464. American Board of Pediatrics. MOC Overview. Accessed Dec 14, 2016. https://www.abp.org/content/moc-overview. Simon TD, et al. Pediatric Medical Complexity Algorithm: a new method to stratify children by medical complexity. Pediatrics. 2014;133:e1647–e1654. Hoying C, et al. “On the scene”: Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio. Nurs Adm Q. 2014;38:27–54. Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN). Welcome to PR-COIN. Accessed Dec 14, 2016. https://pr-coin.org/.

112

Jennifer Lail, MD, FAAP, et al

42. Luria JW, et al. Reliability science and patient safety. Pediatr Clin North Am. 2006;53:1121–1133. 43. Alwan LC, Roberts HV. Time-series modeling for statistical process control. J Bus Econ Stat. 1988;6:87–95. 44. Billett AL, et al. Exemplar pediatric collaborative improvement networks: achieving results. Pediatrics. 2013;131(suppl 4):S196–S203. 45. Lannon CM, Miles PV. Pediatric collaborative improvement networks: bridging quality gaps to improve health outcomes. Pediatrics. 2013;131(suppl 4):S187–S188. 46. Lannon CM, Miles PV, Stockman JA 3rd. The path forward: collaborative networks and the future for children’s health care. Pediatrics. 2013;131(suppl 4):S226– S227.

Chronic Care Model Improves Pediatric Outcomes 47. Lannon CM, Peterson LE. Pediatric collaborative improvement networks: background and overview. Pediatrics. 2013;131(suppl 4):S189–S195. 48. Lannon CM, Peterson LE. Pediatric collaborative networks for quality improvement and research. Acad Pediatr. 2013;13(6 suppl):S69–S74. 49. Association of Maternal & Child Health Programs. Developing structure and process standards for systems of care serving children and youth with special health care needs. A white paper from the national consensus framework for systems of care for children with special health care needs project. Mar 2014. Accessed Dec 14, 2016. http://www .amchp.org/AboutAMCHP/Newsletters/member-briefs/ Documents/Standards%20White%20Paper%20FINAL.pdf.