Final Research Report
View this project's final research report.
Related Journal Citations
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
Study Registration Information
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