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Data-Driven Clinical and Cost Pathways for Chronic Care Delivery

Yiye Zhang, PhD, and Rema Padman, PhD
The authors illustrate a methodology for delineating variations in medical costs for patients with similar clinical conditions and needs using electronic health record data.
Figure 1 displays the overlap in the assignment of patients to subgroups under clinically focused and cost-centered approaches. Although 97% of the high-spending patients were also part of the high-complexity group, 2 patients in the high-spending group—including the patient who spent the most, $18,801.40—were in fact assigned to the medium complexity group. One potential explanation, which needs to be verified by the clinicians, is that there might have been excessive spending of medical resources given these patients’ clinical needs, which are at the medium-complexity level.

In contrast, Figure 2 (parts a and b) displays the large variations in cost among clinically similar patients. Figure 2(b) displays patients’ actual CPs generated for the medium-complexity subgroup. It can be pictured as a map of visits that patients experienced during the 2-year period. Each node in Figure 2 is a unique visit characterized by encounter type, diagnoses noted, and combinations of drug classes prescribed to patients. Starting with the dense, overlapped areas at the left-hand side of Figure 2(b), with all patients diagnosed with CKD stage 3, diabetes, and hypertension, but no other complications, the fanning pathways show the divergence from the common starting point as patients’ disease progresses and complications emerge in diverse ways, for which varying treatments are provided. The size of the nodes and thickness of the edges reflect frequency of visits and transitions in the data. A larger node suggests that this is a common visit that many patients experience, and a thicker edge is an indication that the transition of visits, and accompanying change in clinical conditions or medication prescriptions, are observed among many patients. The color of each visit represents the spending category in quartiles: high (black), medium (dark blue), low (light blue), and zero (green).

DISCUSSION

There is growing recognition of the need for more precise risk-adjustment strategies, incorporation of evidence-based treatment variability, and increased use of data and information technology to facilitate patient engagement and shared decision making in promoting value-based payment systems.4,16,17 In this preliminary study, we aimed to provide a generalizable framework to estimate the costs associated with actual care delivery, and to expose variations in care and cost. In particular, the results from this study showed significant variation in costs among patients who are clinically similar. A deeper analysis of these pathways may uncover the patterns and causes for these variations within subgroups to allow appropriate incorporation of this evidence into the development of future payment models and care delivery practices.

In this study, we analyzed data on CKD patients whose complex, chronic condition is an example of the high-need, high-cost care delivery context that is a continuing challenge to the healthcare system.18 These patients require coordinated care due to their MCCs, and are a key population to be considered in policy design and implementation.4 Our proposed framework specifically targets this patient population by modeling the co-progression of multiple clinical factors, treatments, and medications. We envision that an information technology–enabled tool based on the demonstrated methodology, once developed, deployed, and rigorously evaluated, can be used at the point of care by clinicians and patients to discuss available courses of treatment options, consider their potential efficacy projected at the cohort and personal level, and, equally important, build awareness of the costs associated with the entire course of treatment. Such tools may also provide policy makers and other stakeholders at healthcare practices access to data-driven evidence for innovative cost analyses.

Limitations

Although the methodology is generalizable to other health conditions and includes many clinical factors, a major limitation of our analysis is the accuracy and availability of data, particularly relevant to our cost-estimation approach. For illustrative purposes, we assigned all patients to a single Medicare plan and manually obtained the estimated co-pays from a website that provides prescription price information. Therefore, some of the drugs selected and prescribed to patients that were found to be excessively expensive may well be due to the potential discrepancy between our assumed insurance plan and patients’ actual plans.

In addition, variations in CPs observed within each subgroup may be explained by unobserved variables, such as social and behavioral factors, as well as the more extensive health information that was not included in the current analysis. For example, the nature of our EHR dataset limits our ability to infer medication adherence; thus, the generated CPs assume perfect medication adherence, which we recognize to be unrealistic. There is also the possibility that the lack of adherence may lead to divergences in the CPs, but the current data fail to capture such associations. Furthermore, patients’ conditions are gauged using ICD-9-CM codes recorded in the EHR; therefore, we did not distinguish patients by the severity of each condition. For instance, use of insulin, whose choice is a marker of severity in diabetes,19 is often observed among the high-spending/complexity subgroup, but our CP-learning algorithm considered all patients with diabetes to have the same severity. Availability of such detailed, relevant data in the future—such as claims data and lab results—will help to overcome these limitations.

CONCLUSIONS

This preliminary analysis shows that patient subgroups generated by the CP-learning methods may be able to expose variations in costs among patients who are clinically similar, and vice versa, thereby facilitating future research to develop improved treatment plans within innovative payment models.

Acknowledgments

The authors are grateful to the physicians and staff of the community nephrology practice, who generously provided data from their electronic health record for this study. They particularly thank Pradip Teredesai, MD; Nirav Patel, MD; Qizhi Xie, MD, PhD; and staff members Linda Smith and Audra Barletta, who also gave important clinical and technical information about the data and the key characteristics of chronic kidney disease and its treatments.

Author Affiliations: Division of Health Informatics, Department of Healthcare Policy & Research, Weill Cornell Medicine of Cornell University (YZ), New York, NY; The H. John Heinz III College, Carnegie Mellon University (RP), Pittsburgh, PA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YZ, RP); acquisition of data (YZ, RP); analysis and interpretation of data (YZ, RP); drafting of the manuscript (YZ); critical revision of the manuscript for important intellectual content (YZ, RP); statistical analysis (YZ, RP).

Address Correspondence to: Yiye Zhang, PhD, Assistant Professor, Division of Health Informatics, Department of Healthcare Policy & Research, Weill Cornell Medicine of Cornell University, 425 E 61st St, Ste 301, New York, NY 10065. E-mail: yiz2014@med.cornell.edu.
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