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The American Journal of Managed Care October 2015
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Clinician Considerations When Selecting High-Risk Patients for Care Management
Vivian Haime, BS; Clemens Hong, MD, MPH; Laura Mandel, BA; Namita Mohta, MD; Lisa I. Iezzoni, MD, MSc; Timothy G. Ferris, MD, MPH; and Christine Vogeli, PhD

Clinician Considerations When Selecting High-Risk Patients for Care Management

Vivian Haime, BS; Clemens Hong, MD, MPH; Laura Mandel, BA; Namita Mohta, MD; Lisa I. Iezzoni, MD, MSc; Timothy G. Ferris, MD, MPH; and Christine Vogeli, PhD
Clinicians consider a number of patient predisposing and enabling characteristics not typically available in clinical data systems when selecting high-risk patients for care management.
ABSTRACT

Objectives: Hybrid approaches to case finding show promise as a method to increase the success of care management programs (CMPs). A large healthcare system implemented a hybrid approach in which clinicians review algorithm-generated lists of potential high-risk patients within their practice and select the patients most appropriate for the CMP.  We sought to understand the criteria clinicians used when selecting patients.

Study Design: We conducted 20 semi-structured interviews with a convenience sample of primary care clinicians and their care managers from a diverse set of practices.

Methods: Two independent reviewers thematically coded interview responses through an iterative process.

Results: In addition to clinical need (eg, disease severity or multiple comorbidities), interviewees considered a number of nonclinical patient characteristics that they felt placed patients at risk for suboptimal healthcare utilization and poor adherence. These include patients’ predisposing (eg, health literacy or navigation challenges, physical vulnerabilities, insight regarding their health) and enabling characteristics (eg, social and home environment, coping skills, financial resources). Interviewees also considered patients’ existing linkages with the health system and whether other clinicians were already meeting a patient’s care needs.

Conclusions: In selecting patients for a CMP, clinicians considered patient characteristics that are not easily captured in standard clinical and administrative data. A hybrid approach to case finding concentrates clinician review on patients who meet standard clinical and healthcare utilization criteria, and allows clinicians to incorporate knowledge of patients’ predisposing and enabling characteristics that are not readily maintained in clinical data.

Am J Manag Care. 2015;21(10):e576-e582
Take-Away Points
 
This study identified the factors that primary care clinicians and care managers considered as they reviewed algorithm-generated patient lists to identify a final set of patients appropriate for care management. 
  • In addition to the complexity of patients’ medical conditions, clinicians considered patients’ health literacy, physical vulnerabilities, social and home environment, and coping skills when selecting patients for care management. 
  • Clinicians also considered the extent to which patients’ care needs were met by strong existing linkages to care providers outside of the primary care practice.
Healthcare delivery systems are increasingly entering into risk contracts which require healthcare providers to manage the utilization of defined patient populations. Care management programs (CMPs), particularly those based in primary care practices, have become a common strategy to meet the cost containment and quality targets in these contracts. Due to the concentration of costs in persons with multiple chronic conditions, these persons are often selected for care management interventions that aim to enhance the coordination of health services and improve patients’ ability to navigate the health system.1-4 Appropriate case finding, or the identification of patients most likely to benefit from care management, is key to maximizing the cost-effectiveness and sustainability of CMPs.4,5

Many CMPs rely on quantitative approaches to high-risk patient identification, such as risk prediction models based on prior healthcare utilization and clinical conditions. These approaches typically rely on administrative data, such as billing claims, to predict future healthcare utilization, but are often unable to include more nuanced patient characteristics, including socioeconomic and psychosocial circumstances.6-8 Claims-based approaches often fall short of identifying the patients who are most likely to engage in and benefit from care management because they are unable to detect factors such as patient willingness to participate or barriers to participation.9 Moreover, use of data-driven algorithms often introduces a time lag between when patients need help and when they are identified for the program.10

An alternative strategy is to rely on direct referrals from primary care physicians (PCPs) to identify appropriate patients. This strategy is based on the premise that PCPs know their patients best and can consider patient characteristics not routinely available in administrative data or medical records.6,10 However, PCPs vary widely in their referral patterns for such programs,11-16 and concerns have been raised about underselection of patients with poor existing linkages with the healthcare system.9,16 Finally, physicians with a poor understanding of care management goals or approaches may refer inappropriate patients to these programs.

Hybrid approaches that combine predictive modeling and clinician input have been suggested as a way to overcome the biases and limitations of the individual approaches.10,15 Prior research has explored the extent to which primary care clinician case finding agrees with predictive modeling case finding,10,17 but has not examined the added benefit of clinical review of a list of potentially high-risk patients generated from a predictive algorithm. We investigated the contribution of clinician input in the selection of high-risk patients within an active primary care-based CMP using semi-structured interviews of PCPs and their nurse care managers (CM). We were particularly interested in the criteria and patient factors clinicians used in practice when determining which potential high-risk patients within their panel should be targeted for care management. Specifically, we examined the extent to which clinicians considered: 1) patient characteristics that are and are not routinely available in clinical and billing data; and 2) existing patient linkages to other providers within the healthcare system when reviewing lists of potential high-risk patients, and identifying patients appropriate for practice-based care management. We hypothesized that clinician involvement enhanced the case selection process by allowing the inclusion of information about the patients not readily available in administrative data.

METHODS
Description of Care Management Program and Patient Selection

The CMP is a primary care-based, nurse-led longitudinal care program designed to help high-risk patients (ie, chronically ill, medically complex patients) better manage their health and healthcare utilization. Approximately 400,000 Medicare and commercially insured patients 18 years or older were eligible for the CMP in 2013. CMP patient selection starts with the use of a billing claims-based predictive algorithm to identify an initial list of high-risk patients. As a first step, chronic conditions, utilization patterns and a predictive risk score for future total medical expense are generated using the prior year of paid claims using Optum ImpactPro version 6.0 software (Optum Inc, Eden Prairie, Minnesota). Combinations of specific chronic conditions and patterns of healthcare utilization, as well as overall risk score, are then used to identify the initial set of high-risk patients using an algorithm developed with input from an advisory committee of operational and clinical experts within the healthcare organization. Approximately 5% of patients are identified as potentially high risk based on the claims-based algorithm. These patients are then sorted into PCP-specific lists for clinical review. Lists provided to PCPs included the chronic conditions, utilization, and predictive risk score for each patient, as well as patient name and medical number so clinicians could easily recognize their patients. Each PCP, together with their CM, was responsible for reviewing between 1 and 39 potentially high-risk patients for 2 insurance-risk contracts in 2013, the second year of the program. Overall, clinicians selected approximately 61% of patients on the algorithm-generated list for the CMP. Clinicians were asked to classify patients on their list according to whether they were appropriate for the CMP, or they were not appropriate either because they were not considered high-risk or because the patient’s care needs were already being met. PCPs were given considerable latitude in how they classified and selected their patients for the CMP.

Study Design

Using semi-structured interviews of PCP and CM dyads, we elicited and analyzed the patient characteristics that clinicians considered when selecting patients for CMP. This project was undertaken as a quality improvement initiative, and as such, was not formally supervised by Partners Healthcare’s Institutional Review Board per their policies.

Participants

We identified a convenience sample of 20 PCP and CM dyads from 18 ambulatory practices within an integrated healthcare system consisting of 270 primary care practices located in eastern Massachusetts. Physician organization leaders identified participants with the intention of maximizing variation in practice characteristics, such as the level of affiliation with the delivery system, location, and type (Table 1). The CMP director then confirmed willingness to participate.

Data Collection

A physician researcher (CH) and project manager (VH) conducted all interviews, either by phone or in person, at the respective practices. Interviews lasted 45 to 60 minutes and were audio recorded with consent from all participants. All interviews were completed between May and September 2013. A research assistant (LM) transcribed the audiotapes verbatim.

Interviews followed a semi-structured, open-ended interview guide developed through an iterative process by the research team, and designed to obtain general programmatic quality improvement information, as well as information on the characteristics of patients selected for care management. Relevant interview questions included (see the eAppendix [available at www.ajmc.com] for the complete interview guide): 1) “How do you define a ‘high-risk’ patient and what criteria do you use to determine if a patient is ‘high-risk’?” 2) “What types of patients did you remove because they were ‘not high-risk’?” 3) “What types of patients did you remove because they have their care needs met elsewhere?”

Data Analysis

The Andersen Model of Health Services Utilization18 provided a basic framework for organizing interview responses according to whether they reflected patient predisposition to use services (predisposing factors), characteristics that enable or impede healthcare use (enabling factors), and patient need for healthcare (need). We coded and analyzed interview responses using QSR International’s software, NVivo version 9, a qualitative data management and analysis software tool (QSR International Pty Ltd, Doncaster, Australia). In the first level of analysis, an interviewer and a research assistant (VH and LM) independently read and thematically coded all interview transcripts—a process that allows coders to organize and evaluate similar quotations by themes.19 Through an iterative process, content-related minor themes or factors were collapsed into major factors, then minor and major factors were discussed until a mutual agreement was reached. We met with clinician researchers and operational leaders to solicit feedback, present disconfirming evidence, and validate the coding scheme.20 After coding the data into this framework, we retained and summarized the major and minor predisposing, enabling, and need factors mentioned in at least 20% of interviews. Exploratory analyses examined variations in themes by practice affiliation and model.

RESULTS
Although limited to a single large integrated delivery system, our convenience sample of interviewed practices represents a broad range of practice affiliations, locations, and models (Table 1). The major and minor factors that PCPs and CMs considered in deciding which patients would be appropriate for care management within their primary care practice are summarized in Table 2 and exemplary quotations are provided in Table 3.

Patient Predisposing Factors

Health literacy and navigation. Most interviewees (85%) considered a patient’s ability to comprehend medical instructions and navigate the healthcare system when determining whether patients were appropriate for the CMP. Specifically, more than half (55%) of interviewed PCPs and CMs reported that they considered patient need for education about their diseases and appropriate management as they selected high-risk patients. Almost half of interviewees suggested that activated patients (ie, patients who are good “advocates for themselves,” “well-versed and knowledgeable about what they need,” and “can take care of themselves”) may not benefit from the additional resources provided by care management. On the other hand, interviewees felt that patients with linguistic or cultural barriers, as well as individuals with impaired executive function, would be more likely to benefit from the services rendered by CMs.

Physical vulnerability. More than half (60%) of interviewees considered whether their patients had physical vulnerabilities, including a high risk of falling, frailty, or advanced age, when determining appropriateness for CMP. Clinicians reported that, in some cases, they would select patients with relatively stable functional status if they had physical vulnerabilities because they felt care management might help facilitate patient care in the event of decompensation.

 
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