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The American Journal of Managed Care September 2019
VA Geriatric Scholars Program’s Impact on Prescribing Potentially Inappropriate Medications
Zachary Burningham, PhD; Wei Chen, PhD; Brian C. Sauer, PhD; Regina Richter Lagha, PhD; Jared Hansen, MStat; Tina Huynh, MPH, MHA; Shardool Patel, PharmD; Jianwei Leng, MStat; Ahmad Halwani, MD; and B. Josea Kramer, PhD
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Ravi B. Parikh, MD, MPP; Sachin H. Jain, MD, MBA; and Amol S. Navathe, MD, PhD
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Jan E. Berger, MD, MJ
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Jean P. Hall, PhD; Tracey A. LaPierre, PhD; and Noelle K. Kurth, MS
Multi-Payer Advanced Primary Care Practice Demonstration on Quality of Care
Musetta Leung, PhD; Christopher Beadles, MD, PhD; Melissa Romaire, PhD; and Monika Gulledge, MPH; for the MAPCP Evaluation Team
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Which Patients Are Persistently High-Risk for Hospitalization?
Evelyn T. Chang, MD, MSHS; Rebecca Piegari, MS; Edwin S. Wong, PhD; Ann-Marie Rosland, MD, MS; Stephan D. Fihn, MD, MPH; Sandeep Vijan, MD; and Jean Yoon, PhD, MHS

Which Patients Are Persistently High-Risk for Hospitalization?

Evelyn T. Chang, MD, MSHS; Rebecca Piegari, MS; Edwin S. Wong, PhD; Ann-Marie Rosland, MD, MS; Stephan D. Fihn, MD, MPH; Sandeep Vijan, MD; and Jean Yoon, PhD, MHS
Most patients in a large integrated healthcare system who were high-risk for hospitalization were at substantially lower risk within 2 years.
Our results confirm published findings described in other predictive models for high utilization, readmission, and mortality: Age, gender, number of zip code changes, number of discharges against medical advice,6,7,29 frequent use of outpatient medical and mental health services,6 chronic pain,6 nonadherence, and tobacco use29 significantly predicted long-term persistence of high risk. Other known predictors of high utilization (eg, socioeconomic status, depression, alcohol use disorder, marital status) were not included in our model due to endogeneity. Unlike previous studies, we report that hearing and visual impairment, cancer diagnosis, and palliative care utilization were also predictors of patients being persistently high-risk. Interestingly, unlike prior studies6,29 that preceded the VHA comprehensive primary care teams for homeless patients,30 our findings show that homeless patients who used housing support services were less likely to be persistently high-risk. It is possible that VHA programs that address housing instability were able to effectively change the duration for which a patient is high-risk.

Developing effective programs for high-risk patients has proved challenging because most randomized trials in this area have not shown improvement in outcomes.26,31,32 One possibility is that high-risk patients are so heterogeneous that a one-size-fits-all approach may not be effective. Different clinical interventions may be more suitable for patients who are persistently high-risk versus those who are only initially or intermittently high-risk or for patients with various combinations of factors that predict risk.33,34 For instance, persistently high-risk patients may benefit from longitudinal primary care programs tailored for specific vulnerable populations (eg, primary care for homeless, homebound, or elderly patients; those receiving dialysis; those with chronic pain or serious mental illness); other high-risk patients may benefit from time-limited care management models that augment primary care, such as collaborative care models for high-needs patients.26,35 Large healthcare systems may find it helpful to use empiric clustering models to further distinguish subgroups of high-risk patients based on clinical characteristics.4,5,36,37

Our findings suggest ways to tailor care management interventions for patients with sustained needs. Although the extent to which a patient’s underlying risk for hospitalization is modifiable remains unclear, evidence-based practices can be implemented to possibly reduce a patient’s risk for hospitalization. For instance, interventions to target housing instability, such as the Housing First model, may reduce a patient’s risk for future hospitalization.30 Healthcare teams who target persistently high-risk patients could include behavioral health specialists familiar with managing treatment nonadherence, holistic approaches to chronic pain, and guideline-concordant care for ambulatory care–sensitive conditions (eg, congestive heart failure, diabetes, hypertension). Comprehensive assessments could identify depression and patient factors underlying nonadherence and social instability in high-risk patients and then prioritize which patients should receive the most intensive interventions to reduce future risk. Future studies could examine whether intervening on predictors of persistent high risk is effective in changing a patient’s risk trajectory.

Limitations

Measures of socioeconomic status, including income, employment status, and education status, were likely predictors of risk persistence, but they were not included due to lack of availability in VHA administrative data.29 To avoid a problem in tautology, we excluded VHA risk prediction model variables (eg, mental health conditions, cognitive impairment) from our model to predict whether or not patients remain at high risk. However, we do report some clinical characteristics not included in the risk prediction model that contribute to hospitalization risk, such as chronic pain conditions, markers of social instability, and sensory impairments. Lastly, our sample is limited to the VHA population, which includes veterans who are less likely to have childhood-onset medical conditions due to military medical eligibility, are more likely to have psychiatric conditions, and may use services in multiple healthcare settings, not just the VHA.38 We were unable to draw from non-VHA data sources for this analysis to account for healthcare services that were not paid by the VHA. Although the actual percentage of persistently high-risk patients might differ by payer,39 the overall patterns are likely to be similar.

CONCLUSIONS

Because most high-risk patients may be only briefly high-risk for hospitalization, nonrandomized evaluations of interventions that identify patients based on a single risk score may appear to have positive effects on patient health due to naturally decreasing risk over time in the majority of patients. Identified predictors of patients remaining persistently high-risk could inform efforts to tailor the intensity and type of interventions to improve health for veterans with the most sustained needs.

Acknowledgments

This study was funded by the Veterans Health Administration (VHA) Patient Aligned Care Team Demonstration Laboratory Coordination Center (XVA-61-041). Dr Wong is supported by VHA Career Development Award CDA 13-024. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the University of California, the University of Washington, the University of Pittsburgh, or the University of Michigan.

Author Affiliations: Center for the Study of Healthcare Innovation, Implementation and Policy, Veterans Affairs (VA) Greater Los Angeles Health Care System (ETC), Los Angeles, CA; David Geffen School of Medicine, University of California Los Angeles (ETC), Los Angeles, CA; Office of Clinical Systems Development and Evaluation, Veterans Health Administration (RP), Washington, DC; Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System (ESW), Seattle, WA; Department of Health Services (ESW) and Department of Medicine (SDF), University of Washington, Seattle, WA; Center for Health Equity Research and Promotion, VA Pittsburgh (AMR), Pittsburgh, PA; Department of Medicine, University of Pittsburgh (AMR), Pittsburgh, PA; Center for Clinical Management Research, VA Ann Arbor (SV), Ann Arbor, MI; Department of Internal Medicine, University of Michigan (SV), Ann Arbor, MI; VA Health Economics Resource Center (JY), Menlo Park, CA; VA Center for Innovation to Implementation (JY), Menlo Park, CA; Department of General Internal Medicine, University of California San Francisco School of Medicine (JY), San Francisco, CA.

Source of Funding: This study was funded by the Veterans Health Administration Patient Aligned Care Team Demonstration Laboratory Coordination Center (XVA-61-041).

Author Disclosures: Dr Wong reports prior ownership of common stock in UnitedHealth Group Inc. Dr Vijan has a pending VA grant on a similar subject. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ETC, ESW, AMR, SDF, JY); acquisition of data (ETC, RP); analysis and interpretation of data (ETC, RP, ESW, AMR, SV, JY); drafting of the manuscript (ETC, RP, SDF); critical revision of the manuscript for important intellectual content (ETC, RP, ESW, AMR, SDF, SV, JY); statistical analysis (ETC, RP, ESW, SV); provision of patients or study materials (ETC); obtaining funding (ETC); administrative, technical, or logistic support (ETC); and supervision (ETC).

Address Correspondence to: Evelyn T. Chang, MD, MSHS, VA Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Los Angeles, CA 90073. Email: Evelyn.Chang@va.gov.
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