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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
The Sociobehavioral Phenotype: Applying a Precision Medicine Framework to Social Determinants of Health
Ravi B. Parikh, MD, MPP; Sachin H. Jain, MD, MBA; and Amol S. Navathe, MD, PhD
From the Editorial Board: Jan E. Berger, MD, MJ
Jan E. Berger, MD, MJ
Medicaid Managed Care: Issues for Enrollees With Serious Mental Illness
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
Physician-Initiated Payment Reform: A New Path Toward Value
Suhas Gondi, BA; Timothy G. Ferris, MD, MPH; Kavita K. Patel, MD, MSHS; and Zirui Song, MD, PhD
Managed Care for Long-Stay Nursing Home Residents: An Evaluation of Institutional Special Needs Plans
Brian E. McGarry, PT, PhD; and David C. Grabowski, PhD
Changes in Ambulatory Utilization After Switching From Medicaid Fee-for-Service to Managed Care
Lisa M. Kern, MD, MPH; Mangala Rajan, MBA; Harold Alan Pincus, MD; Lawrence P. Casalino, MD, PhD; and Susan S. Stuard, MBA
Did Medicare Advantage Payment Cuts Affect Beneficiary Access and Affordability?
Laura Skopec, MS; Joshua Aarons, BA; and Stephen Zuckerman, PhD
Medicare Shared Savings Program ACO Network Comprehensiveness and Patient Panel Stability
Cassandra Leighton, MPH; Evan Cole, PhD; A. Everette James, JD, MBA; and Julia Driessen, PhD
Currently Reading
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.
ABSTRACT

Objectives: Many healthcare systems use prediction models to estimate and manage patient-level probability of hospitalization. Patients identified as high-risk at one point in time may not, however, remain high-risk. We aimed to describe subgroups of patients with distinct longitudinal risk score patterns to inform interventions tailored to patients’ needs.

Study Design: Retrospective national cohort study.

Methods: Using a previously validated prediction algorithm, we identified a cohort of 258,759 patients enrolled in the Veterans Health Administration (VHA) who were in the top 5% of risk for hospitalization within 90 days. During each of the following 24 months, patients were placed in 1 of 6 categories: death, hospitalized, no VHA care, persistently high-risk for hospitalization (≥10% probability), initially high-risk then persistently low-risk (<10% probability), and intermittently high-risk. We used multivariable logistic regression to identify characteristics predictive of being persistently high-risk through the last study month.

Results: After 2 years, 17.7% had died, 13.8% had remained persistently high-risk for hospitalization, 41.5% had become persistently low-risk, and 19.9% were intermittently high-risk. Predictors of being persistently high-risk included urban residence, chronic medical comorbidities, auditory and visual impairment, chronic pain, any cancer diagnosis, and social instability.

Conclusions: Few patients who were high-risk for hospitalization at baseline remained so. Nonrandomized evaluations of interventions that identify patients based on a single high-risk score may spuriously appear to have positive effects. Clinical interventions may need to focus on individuals who are persistently high-risk.

Am J Manag Care. 2019;25(9):e274-e281
Takeaway Points

Many patients identified as high-risk for near-term hospitalizations using a risk prediction model at baseline did not remain high-risk over a 2-year period.
  • After 2 years, just 14% remained persistently high-risk for hospitalization. Of the remainder, 20% were intermittently high-risk, 42% became persistently low-risk, and 18% had died.
  • Predictors of being persistently high-risk included age, gender, urban residence, chronic pain, any cancer diagnosis, chronic medical comorbidities, and social instability.
  • Nonrandomized evaluations of interventions that target patients based on a single high-risk score may spuriously appear to improve patient outcomes due to natural decreasing risk over time.
The development of advanced healthcare analytics allows large healthcare organizations to use risk stratification as a population management strategy to proactively identify patients at high risk for poor health outcomes. These high-risk patients may benefit from customized interventions with the goals of improving outcomes and providing healthcare more efficiently.1 Risk stratification or risk prediction tools are often based on historical administrative claims and utilization data.2 However, the stability of risk estimates over time is uncertain. Programs that target patients identified as being high-risk at one point in time do not take into account the dynamic nature of changing risk over time; therefore, such programs may vary in effectiveness across patients.1 Different clinical interventions may be indicated for patients who are persistently versus temporarily high-risk.3-5

Studies have supported the notion that many high-cost, high-needs patients are just temporarily high-risk.3-5 Documented predictors of hospital and emergency department (ED) utilization include schizophrenia, homelessness, opioid prescriptions, depression, substance use, social isolation, and heart failure.6-8 However, little information exists to characterize patients who remain persistently high-risk for hospitalization. Prediction of persistent high risk over time may enhance risk stratification as a population management strategy and inform efforts to target and tailor resource-intensive clinical interventions.

The Veterans Health Administration (VHA) incorporated risk stratification measures into practice nationally in 2012. It reports weekly estimates of the probability of patient hospitalization in the next 90 days for the entire primary care patient population for use by primary care teams to inform clinical decision making.9-11 We aimed to inform population management programs for high-risk patients12 by following a cohort of high-risk patients over a 2-year period. Our objectives were to identify and describe groups of high-risk patients with distinct longitudinal risk score patterns and identify baseline characteristics that predicted which patients were likely to remain high-risk over time.

METHODS

Data Sources

Data for this study were obtained from the VHA Corporate Data Warehouse, which included patient demographics, clinical diagnoses, admission and discharge status, patient risk scores, and utilization records.13 We also obtained data from the VHA Vital Status file to ascertain patients’ dates of death.

Study Sample

We identified a national cohort of 258,759 patients who had a 10% or greater absolute probability of 90-day hospitalization on our index date (November 23, 2012), were assigned to a VHA primary care provider, and were not hospitalized in the VHA on the index date, because risk scores are not generated for hospitalized patients. We followed patients for 2 years after the index date.

Measurement of Risk

We obtained patients’ monthly risk using the predicted probability of hospitalization in the next 90 days in the Care Assessment Need (CAN) model, a validated risk prediction algorithm.9 The CAN model (eAppendix Table 1 [eAppendix available at ajmc.com]) uses demographics, utilization of VHA health services, comorbidities, prescribed medications, vital signs, and veteran-specific variables.9

Risk scores were generated on a weekly basis for all patients in VHA primary care. Patient-month measures of risk were calculated as the mean of weekly probabilities for that calendar month, and missing values occurred when veterans were hospitalized (including acute care, residential treatment facility admission, nursing home admission, or hospice admission) or dropped out of VHA care.

Dependent Variables

We defined patients as “high-risk” if the probability of 90-day hospitalization was 10% or greater, which corresponds to approximately the top 5% of VHA patients (n = 258,759). Our main dependent variable was a patient-month measure of risk persistence, defined hierarchically (Figure 1).

Patients with at least 1 missing CAN probability in a calendar month were categorized into 1 of the following mutually exclusive outcomes: death, hospitalization in VHA, or dropped out of VHA care (no VHA encounters in previous 24 months). By definition, patients can move into and out of the “hospitalization” or “dropped out of care” categories. All patients except those who died were recategorized the following month.


 
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