Results Summary

What was the project about?

Patients with high healthcare needs include those with multiple health problems, like diabetes and heart disease. Current methods to identify patients with high healthcare needs aren’t always accurate. These methods may miss some patients such as those with mental illness or social risks like unstable housing. Better methods could help doctors make sure these patients are getting the care they need.

In this study, the research team developed a method to predict which patients may have high healthcare needs.

What did the research team do?

First, the research team created a new system for identifying patients who have high healthcare needs. The team organized patients into 10 groups based on different patient traits, health problems, and risks, such as chronic pain, mental illness, and unemployment. The team then tested the system to see if it could identify patients with high healthcare needs. To do so, the team used data from 428,024 patients receiving care at New York City health centers. They also used data about the neighborhoods where patients lived. All patients had Medicare insurance.

Using the 10 groups, the research team created a statistical method to predict if a patient would have high healthcare needs. The team checked how different groups and other patient data, like age and gender, improved how accurate the method was. They tested the method with data from 1,074,389 patients receiving care from New York City health centers from 2013 to 2016. The team confirmed the results using data from health systems in Florida.

Patients, caregivers, doctors, and health system administrators helped design the study.

What were the results?

The new grouping system identified 99 percent of patients with high healthcare needs.

The statistical method predicted which patients were likely to have high healthcare needs. The method was most accurate when it had patient data on all 10 groups plus age and gender.

What were the limits of the project?

The research team used data from patients with Medicare insurance in two states. The results may differ for patients with other types of insurance or from other locations.

Future studies could test the method using data from patients with other types of insurance or in other regions.

How can people use the results?

Researchers and doctors can use the methods to help make sure patients with high healthcare needs get the care they need.

How this project fits under PCORI’s Research Priorities
The PCORnet® Study reported in this results summary was conducted using PCORnet®, the National Patient-Centered Clinical Research Network. PCORnet® is intended to improve the nation’s capacity to conduct health research, particularly comparative effectiveness research (CER), efficiently by creating a large, highly representative network for conducting clinical outcomes research. PCORnet® has been developed with funding from the Patient-Centered Outcomes Research Institute® (PCORI®).

Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments. 

Peer reviewers commented and the researchers made changes or provided responses. Those comments and responses included the following:

  • The reviewers pointed out that there is a distinction between patients with high health needs and patients with high health costs, because research has demonstrated that patients from underrepresented minority groups often face discrimination and less access to health care, so high-needs patients from these communities may not have high healthcare costs. They recommended that the researchers instead use a proxy of number of chronic conditions to identify high-needs patients rather than costs. The researchers agreed with this consideration and analyzed the data based on this premise to determine whether their results remained consistent. They compared the number of chronic conditions by race and socioeconomic status for each level of predicted cost-based patient risk, and found no biases based on race or socioeconomic status in their results. However, they acknowledged that the databases they used to collect health needs may themselves have biased information because the data may be incomplete for patients from disadvantaged groups with less access to care.
  • The reviewers noted the poor performance of the machine-learning models compared to logistic regression, in predicting healthcare costs. The researchers agreed that the machine-learning models did not perform well and agreed with reviewers that this was probably due to them using only 10 predictor variables in calculating those models via machine learning.
  • The reviewers asked the researchers to discuss their proposed approach to predicting high-cost patients in comparison to other prediction methods that the researchers mentioned in the background section of the report. The researchers pointed out that they could not readily compare their methods to some of the other prediction methods because of different definitions for what percent of annual health spending constituted high cost, different populations of patients, and different goals for developing the taxonomy predicting high-cost patients. 

Conflict of Interest Disclosures

Project Information

Rainu Kaushal, MD, MPH
Joan & Sanford I. Weill Medical College of Cornell University
$2,307,765
10.25302/07.2021.HSD.1604356187
Identifying and Predicting Patients with Preventable High Utilization

Key Dates

June 2016
October 2022
2016
2021

Study Registration Information

Tags

Has Results
Populations Populations PCORI is interested in research that seeks to better understand how different clinical and health system options work for different people. These populations are frequently studied in our portfolio or identified as being of interest by our stakeholders. View Glossary
State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: November 30, 2022