Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation.

BACKGROUND: Addressing both clinical and nonclinical determinants of health is essential for improving population health outcomes. In 2012, the Johns Hopkins Community Health Partnership (J-CHiP) implemented innovative population health management programs across acute and community environments. The community-based program involved multidisciplinary teams [ie, physicians, care managers (CM), health behavior specialists (HBS), community health workers, neighborhood navigators] and collaboration with community-based organizations to address social determinants. OBJECTIVES: To report the impact of a community-based program on cost and utilization from 2011 to 2016. DESIGN: Difference-in-difference estimates were calculated for an inclusive cohort of J-CHiP participants and matched nonparticipants. The analysis was replicated for participants with a CM and/or HBS to estimate the differential impact with more intensive program services. SUBJECTS: A total of 3268 high-risk Medicaid and Medicare beneficiaries (1634 total J-CHiP participants, 1365 with CM and 678 with HBS). OUTCOME MEASURES: Paid costs and counts of emergency department visits, admissions, and readmissions per member per year. RESULTS: For Medicaid, costs were almost $1200 per member per year lower for participants as a whole, $2000 lower for those with an HBS, and $3000 lower for those with a CM; hospital admission and readmission rates were 9%-26% lower for those with a CM and/or HBS. For Medicare, costs were lower (-$476), but utilization was similar or higher than nonparticipants. None of the observed Medicaid or Medicare differences were statistically significant. CONCLUSIONS: Although not statistically significant, the results indicate a promising innovation for Medicaid beneficiaries. For Medicare, the impact was negligible, indicating the need for further program modification.

Key Design Considerations When Calculating Cost Savings for Population Health Management Programs in an Observational Setting.

OBJECTIVE: To illustrate the impact of key quasi-experimental design elements on cost savings measurement for population health management (PHM) programs. DATA SOURCES: Population health management program records and Medicaid claims and enrollment data from December 2011 through March 2016. STUDY DESIGN: The study uses a difference-in-difference design to compare changes in cost and utilization outcomes between program participants and propensity score-matched nonparticipants. Comparisons of measured savings are made based on (1) stable versus dynamic population enrollment and (2) all eligible versus enrolled-only participant definitions. Options for the operationalization of time are also discussed. DATA COLLECTION/EXTRACTION METHODS: Individual-level Medicaid administrative and claims data and PHM program records are used to match study groups on baseline risk factors and assess changes in costs and utilization. PRINCIPAL FINDINGS: Savings estimates are statistically similar but smaller in magnitude when eliminating variability based on duration of population enrollment and when evaluating program impact on the entire target population. Measurement in calendar time, when possible, simplifies interpretability. CONCLUSION: Program evaluation design elements, including population stability and participant definitions, can influence the estimated magnitude of program savings for the payer and should be considered carefully. Time specifications can also affect interpretability and usefulness.

Population health: a nursing action plan.

The passage of the Affordable Care Act shifted the focus of health care from individual, patient specific, episodic care, towards health management of groups of people with an emphasis on primary and preventive care. Population health management assists to attain and maintain health while improving quality and lowering costs. The recent Catalyst for Change report creates an urgent call for harnessing the power of nurses-in our communities, schools, businesses, homes and hospitals-to build capacity for population health. Informatics Nurse Specialists are prepared to bridge roles across practice, research, education, and policy to support this call. Each year, the AMIA Nursing Informatics Working Group convenes an expert panel to reflect on the "hot topics" of interest to nursing. Not surprisingly, the 2017 topic was on the current state and challenges of population health. The following summary reflects the panel's perspectives and recommendations for action.

The Doctor of Nursing Practice: From Start-Up to Impact.

AIM: This article reflects on the progress of the doctor of nursing practice (DNP) degree and its place in health care. BACKGROUND: The DNP originated over 10 years ago, long enough for a comprehensive evaluation. METHOD: Rogers' Diffusion of Innovation Theory is used to trace the history of the DNP. Nurse leaders from service and academia (n = 120) share strategies and innovations, and evaluate DNP education with a focus on outcomes and impact. RESULTS: As schools of nursing target DNPs to become faculty to mitigate the shortage, participants agreed it is time to focus on graduating strong leaders prepared to transform health care. CONCLUSION: A growing number of nurses practicing in diverse roles have earned the DNP from programs that vary considerably in rigor. Demand for the competencies, skills, and experience which DNPs bring to practice is high as organizations adapt to the accountable care environment.

A method for estimating cost savings for population health management programs.

OBJECTIVE: To develop a quasi-experimental method for estimating Population Health Management (PHM) program savings that mitigates common sources of confounding, supports regular updates for continued program monitoring, and estimates model precision. DATA SOURCES: Administrative, program, and claims records from January 2005 through June 2009. DATA COLLECTION/EXTRACTION METHODS: Data are aggregated by member and month. STUDY DESIGN: Study participants include chronically ill adult commercial health plan members. The intervention group consists of members currently enrolled in PHM, stratified by intensity level. Comparison groups include (1) members never enrolled, and (2) PHM participants not currently enrolled. Mixed model smoothing is employed to regress monthly medical costs on time (in months), a history of PHM enrollment, and monthly program enrollment by intensity level. Comparison group trends are used to estimate expected costs for intervention members. Savings are realized when PHM participants' costs are lower than expected. PRINCIPAL FINDINGS: This method mitigates many of the limitations faced using traditional pre-post models for estimating PHM savings in an observational setting, supports replication for ongoing monitoring, and performs basic statistical inference. CONCLUSION: This method provides payers with a confident basis for making investment decisions.

Work limitations and their relationship to morbidity burden among academic health center employees with diabetes.

The objective of this study was to determine the prevalence of work limitations and their relationship to morbidity burden among academic health center employees with diabetes. Employees with diabetes were surveyed via Internet and mail using the Work Limitations Questionnaire. Morbidity burden was measured using the Adjusted Clinical Groups methodology. Seventy-two percent of the employees with diabetes had a work limitation. Adjusted odds ratios for overall, physical, time, and output limitations were 1.81, 2.27, 2.13, and 2.14, respectively. Morbidity burden level is an indicator of work limitations in employees with diabetes and can be used to identify employees who may benefit from specialized services aimed at addressing their work limitations associated with diabetes.

Guided care: cost and utilization outcomes in a pilot study.

Guided Care (GC) is an enhancement to primary care that incorporates the operative principles of disease management and chronic care innovations. In a 6-month quasi-experimental study, we compared the cost and utilization patterns of patients assigned to GC and Usual Care (UC). The setting was a community-based general internal medicine practice. The participants were patients of 4 general internists. They were older, chronically ill, community-dwelling patients, members of a capitated health plan, and identified as high risk. Using the Adjusted Clinical Groups Predictive Model (ACG-PM), we identified those at highest risk of future health care utilization. We selected the 75 highest-risk older patients of 2 internists at a primary care practice to receive GC and the 75 highest-risk older patients of 2 other internists in the same practice to receive UC. Insurance data were used to describe the groups' demographics, chronic conditions, insurance expenditures, and utilization. Among our results, at baseline, the GC (all targeted patients) and UC groups were similar in demographics and prevalence of chronic conditions, but the GC group had a higher mean ACG-PM risk score (0.34 vs. 0.20, p < 0.0001). During the following 6 months, the GC group had lower unadjusted mean insurance expenditures, hospital admissions, hospital days, and emergency department visits (p > 0.05). There were larger differences in insurance expenditures between the GC and UC groups at lower risk levels (at ACG-PM = 0.10, mean difference = $4340; at ACG-PM = 0.6, mean difference = $1304). Thirty-one of the 75 patients assigned to receive GC actually enrolled in the intervention. These results suggest that GC may reduce insurance expenditures for high-risk older adults. If these results are confirmed in larger, randomized studies, GC may help to increase the efficiency of health care for the aging American population.

Clinical features of high-risk older persons identified by predictive modeling.

The objective of this study was to describe the clinical features of older persons identified as high risk by a predictive modeling algorithm and to determine their suitability for clinical interventions like case management or disease management. A cross-sectional survey was undertaken at a community-based general internal medicine practice with 826 older patients enrolled in a Medicare-like health plan for military retirees and their dependents. Administrative claims data provided information about all 826 enrollees' chronic conditions, their use of health services, and the cost of those services during the past year. A survey mailed to 150 identified high-risk enrollees provided information about sociodemographic characteristics, general health, bed disability days, restricted activity days, activities of daily living (ADL) limitations, and instrumental activities of daily living (IADL) limitations. Compared to the 676 low-risk enrollees, the 150 high-risk enrollees had higher prevalence of eight individual chronic conditions, higher total chronic conditions (2.93 vs. 1.48, p < 0.001), higher annual rates of hospital admission (1.1 vs. 0.1, p < 0.001), more annual hospital days (7.3 vs. 0.5, p < 0.001), and higher total health insurance expenditures ($22,815 vs. $3,726, p < 0.001). The high-risk respondents to the survey (response rate = 80.0%) had suboptimal health (42.8% "fair or poor"), impaired functional ability (36.3% with 1+ ADL limitations, 58.1% with 1+ IADL limitations), and frequent health-related disruptions in their activities during the previous six months (38.7% with 1+ bed disability day, 52.3% with 1+ restricted activity day). A claims-based predictive modeling algorithm identifies older persons whose health, functional ability, and use of health services suggest they are good candidates for clinical interventions such as case management and disease management.