# Generalizable prediction of medication adherence in heart failure

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $741,786

## Abstract

Project Summary/Abstract
Heart failure (HF) is associated with high rates of hospitalization and mortality. While a number of evidence-
based therapies have been shown to improve outcomes for patients with HF, nearly half of these patients are
not regularly taking their medications. Although medication adherence can be improved through timely
interventions, it is challenging for clinicians to accurately identify and predict medication non-adherence at the
point of care. The challenge persists partly because medication adherence is a complex process influenced by
an interplay of a multitude of patient-, provider-, system-, community-, and therapy-related factors. This gap in
identifying patients at risk of non-adherence can be addressed through increasing availability of relevant data
from electronic health records (EHRs), which affords the potential to make accurate, real time predictions of
adherence in HF. In particular, recent linkages of EHR and pharmacy data has created opportunity for
incorporation of prior medication fills into EHR-based adherence prediction models that are updated
continuously. Using machine learning (ML) techniques with such data allows for incorporation of a large
number of intercorrelated risk factors and their interactions into models and for accommodating continuous
updates as new information becomes available. Our objective is to build a ML-based algorithm to predict
adherence among patients with HF. The specific aims are: 1) to develop supervised ML algorithms to predict
medication adherence among HF patients, using EHR clinical data, linked pharmacy fill data, and location-
based social determinants data from a large, urban health system that cares for a diverse patient population; 2)
to assess fairness of the developed algorithms by evaluating cross-validated prediction and calibration on
patient subgroups based on social and economic factors, to ensure that the desirable prediction performance
is maintained for the diverse groups; and 3) to assess generalizability of the algorithms through validation in a
second large, urban health system caring for a diverse population. Our approach is innovative and novel in
several ways. First, we will take advantage of linkages between pharmacy fill information and the EHR to
incorporate pharmacy data in our models. Second, we utilize geocoding of patient addresses combined with
publicly available data to incorporate neighborhood-level social determinants of health, which are among the
most important predictors of adherence, into our models. Third, we will assess fairness of the model by
evaluating the predictive performance and calibration on patients from diverse backgrounds. Fourth, we will
ensure generalizability of the prediction algorithm by developing it in one diverse health system and validating
the algorithm in a second diverse health system. These models will be developed such that they can be used
for point-of-care adherence prediction. Our long term goal is to b...

## Key facts

- **NIH application ID:** 10795035
- **Project number:** 5R01HL155149-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Samrachana Adhikari
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $741,786
- **Award type:** 5
- **Project period:** 2021-03-15 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10795035

## Citation

> US National Institutes of Health, RePORTER application 10795035, Generalizable prediction of medication adherence in heart failure (5R01HL155149-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10795035. Licensed CC0.

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