# Empirical Classification of the Typologies of Hospital Deaths

> **NIH NIH R21** · DARTMOUTH COLLEGE · 2020 · $246,000

## Abstract

PROJECT ABSTRACT
Measurement of hospital performance through risk-adjusted mortality rates is a cornerstone of the United
States health system, factoring heavily into value-based purchasing and other strategies to incent quality
improvement. Although risk-adjusted mortality captures one of the key functions of a hospital – its ability to
prevent death – it makes no distinction between deaths that result from suboptimal care and those that result
from informed decisions to forego life-sustaining treatment and focus on palliation in anticipation of death. This
results in two critical problems. First, by rewarding hospitals for fewer deaths, these measures create an
incentive to prolong life with aggressive interventions when palliation in anticipation of death may better align
with patient preferences. Second, by failing to distinguish between different types of deaths, these measures
do not provide insight into the specific targets on which a hospital should focus its quality improvement efforts,
since the mechanism underlying the high mortality rate is not known. Ideally, a mortality-based hospital
performance measure would simultaneously (1) reward reductions in deaths that result from suboptimal
disease-directed care or medical errors, (2) reward increases in high-quality palliation in anticipation of death
when consistent with patient preferences, and (3) provide hospitals with actionable information for quality
improvement by offering insight into how patients are dying. A critical barrier to the development of such a
measure is our current inability to distinguish different types of deaths. Missing is a detailed epidemiology of
the typologies of hospital deaths, i.e., “how” hospitalized patients are dying. The goal of this project is to
identify typologies of hospital deaths that can inform new strategies in hospital performance measurement to
improve multidimensional quality of hospital care. We will study patients hospitalized with chronic obstructive
pulmonary disease, acute myocardial infarction, heart failure, pneumonia, and cerebrovascular accident
because they are the subject of public mortality reporting and pay-for-performance initiatives and represent
acute and chronic diseases for which in-hospital decisions regarding end-of-life care may be different. We will
focus particular attention on patients with dementia who may be at disproportionate risk for both under-
treatment (i.e., failure to rescue due to provider bias) and over-treatment (i.e., rescue when quality of life is
already below the patient’s minimal acceptable function). We will use innovative data mining techniques to
identify key clinical variables through mixed-methods analysis of the decision-making processes of institutional
mortality reviewers. Findings from this R21 will be used to support an R01 application designed to externally
validate the typologies with the ultimate goal of developing innovative composite performance measures that
can simultaneously reward ...

## Key facts

- **NIH application ID:** 9875109
- **Project number:** 1R21AG065704-01
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** AMBER E BARNATO
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $246,000
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9875109

## Citation

> US National Institutes of Health, RePORTER application 9875109, Empirical Classification of the Typologies of Hospital Deaths (1R21AG065704-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9875109. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*