Identifying Populations with Complex Needs: Variation in Approaches Used to Select Complex Patient Populations

Davis, Al; Osuji, Thearis A.; Chen, John; Lyons, Lindsay Joe; Gould, Michael K.

doi:10.1089/pop.2020.0153

Cited by 8 publications

(15 citation statements)

References 64 publications

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“…1,53,54 Refined predictive models may help stratify populations by risk level, segment individuals into homogeneous groups, and target those with actionable care needs. 5,55 • Generate evidence on identifying and addressing social determinants of health to support a culture of health in the community served. Effective collaboration will likely require effective and ongoing data sharing between health care delivery and community entities, identifying individuals with MCC who may be interested in community referrals, and engaging the community in establishing priorities for shared efforts to support population health.…”

Section: Discussionmentioning

confidence: 99%

“…Patient‐reported data may also reveal understudied MCC sub‐populations at risk for poor outcomes such as middle age adults who are faced with competing demands and logistical challenges 1,53,54 . Refined predictive models may help stratify populations by risk level, segment individuals into homogeneous groups, and target those with actionable care needs 5,55 Generate evidence on identifying and addressing social determinants of health to support a culture of health in the community served.…”

Section: Discussionmentioning

confidence: 99%

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Emerging models of care for individuals with multiple chronic conditions

Savitz

Bayliss

2021

Health Services Research

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Objective To characterize emerging and current practice models to more effectively treat and support patients with multiple chronic conditions (MCC). Data Sources/Study Setting We conducted a rapid literature scoping augmented by key informant interviews with clinicians knowledgeable about MCC care from a broad spectrum of US delivery systems and feedback from multidisciplinary experts at two virtual meetings. Study Design Literature findings were triangulated with data from semi‐structured interviews with clinical experts. Reflections on early results were obtained from policy, research, clinical, advocacy, and patient representatives at two virtual meetings sponsored by the Agency for Healthcare Research and Quality. Emergent themes addressed were as follows: (1) more timely strategies for MCC care; and (2) trends not previously represented in the peer‐reviewed literature. Data Collection/Extraction Methods The rapid literature scoping relied on Ovid MEDLINE(R) and Epub Ahead of Print databases for the most recent 5‐year period. Qualitative interviews were conducted by telephone. Virtual meetings provided oral and written (chat) captured inputs. Principal Findings Although the literature scoping did not identify a specific set of evidence‐based care models, key informant discussions identified eight themes reflecting emerging approaches to population‐based MCC care. For example, addressing the needs of individuals with MCC through a complexity lens by assessing and addressing social risk factors; extending the care continuum with home‐based care; understanding how to address ongoing patient and caregiver supports outside of clinical encounters; and engaging available community resources. Conclusions Integrating care for MCC patient populations requires processes for determining different subpopulation needs in various settings and lived experiences. Innovation should be anchored at the nexus of payment systems, social risks, medical needs, and community‐based resources. Our learnings suggest a need for an ongoing MCC care research agenda to inform new approaches to care delivery incorporating innovations in technology and home‐based supports for patients and caregivers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Emerging models of care for individuals with multiple chronic conditions

Savitz

Bayliss

2021

Health Services Research

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“…While such criteria sets are sometimes intricate (such as the "impactibility score" described by DuBard et al in 2018) 6 , many patient selection methods in the complex care literature involve one or two criteria defined using simple thresholds. 7 A recent review surveyed the complex care literature to characterize the breadth of current approaches to defining complexity. Among the 90 populations with complex needs reviewed, the authors found no two population definitions that were identical.…”

Section: Introductionmentioning

confidence: 99%

“…Among the 90 populations with complex needs reviewed, the authors found no two population definitions that were identical. 7 While the authors identified common themes in the approaches used to define complex populations, they also noted that there are innumerable ways in which these definitions can be specified. Even within one conceptual approach (e.g., high-cost patients), population criteria may differ in timeframe, thresholds, and other specifications.…”

Section: Introductionmentioning

confidence: 99%

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Defining Complex Patient Populations: Implications for Population Size, Composition, Utilization, and Costs

et al. 2021

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BACKGROUND: Interventions to support patients with complex needs have proliferated in recent years, but the question of how to identify patients with complex needs has received relatively little attention. There are innumerable ways to structure inclusion and exclusion criteria for complex care interventions, and little is known about the implications of choices made in designing patient selection criteria. OBJECTIVE: To provide insights into the design of patient selection criteria for interventions, by implementing criteria sets within a large health plan member population and comparing the characteristics of the resulting complex patient cohorts. DESIGN: Retrospective observational descriptive study. SUBJECTS: Patients identified as having complex needs, within the membership population of Kaiser Permanente Southern California, a large, populationbased health plan with more than 4 million members. We characterize five commonly used archetypes of complex needs: high-cost patients, patients with multiple chronic conditions, frail elders, emergency department high-utilizers, and inpatient high-utilizers. MEASURES: We selected an initial set of criteria for identifying patients in each of the archetypical complex populations, based on available administrative data. We then tested multiple variants of each definition. We compared the resulting patient cohorts using univariate and bivariate descriptive statistics. KEY RESULTS: Overall, 32.7% of the 3,112,797 adults in our population-based sample were selected by at least one of the 25 definitions of complexity we tested. Across definitions the total number of patients identified as complex ranged from 622,560 to 1583 and complex patient cohorts varied widely in demographic characteristics, chronic conditions, healthcare utilization, spending, and survival. CONCLUSIONS: Choice of patient population is critical to the design of complex care programs. Exploratory analyses of population criteria can provide useful information for program planning in the setting of limited resources for interventions. Data such as these should be generated as a key step in program design.

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