Analytics and Coordination of Care in the Healthcare Industry

Atish Sinha
Dr. Atish Sinha at UWM's Connected Systems Institute

Coordination of care in the healthcare setting may not only result in better patient outcomes, but also stands to make a significant dent healthcare costs in the U.S., where 18.3% of gross domestic product was attributed to healthcare spending in 2021. In fact, a 2019 study published in the Journal of the American Medical Association found that failures in care coordination account for $27.2 billion to $78.2 billion in waste per year in the United States.

Increasingly, healthcare organizations are recognizing the benefits of care coordination and are turning to analytics to improve it. While scholars are conducting research in this field, Atish Sinha, Rockwell Automation Endowed Professor in Connected Systems — an expert in artificial intelligence, machine learning, and analytics — says more in-depth study into explanatory analytics and predictive analytics is needed to better inform strategy and decision making. Explanatory analytics uses data to go beyond explaining what happened to focus on how and why it happened. Predictive analytics uses historical data to forecast what might happen in the future.

To identify new analytics solution pathways to care coordination problems, Sinha and his research colleagues (Subodha Kumar of Temple University, Liangfei Qiu of University of Florida, and Arun Sen of Texas A&M University) developed a care coordination framework that looks at the care coordination perspective on one axis (single-provider, cooperative team of providers, or contracted team of providers) and the care coordination mechanism on the other.

The research team analyzed 70 related scholarly articles from the period 1990 through 2020, and conducted a gap analysis to identify future research questions and propose solution approaches.

Their study was published earlier this year in Production and Operations Management.

The findings of the study point to the importance of future research into referral management, care transition, and care integration. The roadmap to making progress in these areas, they say, is four-fold: (1) embracing artificial intelligence, machine learning, and the Internet of Things, (2) exploiting social media data, (3) improving resource alignment, and (4) developing new assessment measures.

Advanced technologies could emerge as powerful enablers for care coordination, Sinha notes. Say a patient has a sudden drop in blood count, post-surgery. That information would be transmitted in real time by the patient’s wearable device to a predictive machine learning model, which could dynamically decide whether the patient should be sent to an acute rehabilitation facility or not.

Future research could also tap into unstructured data from various social media patient forums or review platforms. For example, machine learning techniques could be applied to extract latent topics in patient reviews related to care transition — such as discharge planning, effective communication, or follow-up appointments – to potentially influence a hospital’s objective quality performance measures, such as the time spent in the emergency department, readmission rates, and mortality rate.

Linking the right resources to meet patient needs is one of the major barriers in care coordination. While new technology platforms like Aunt Bertha, Charity Tracker, and Cross TX have emerged to facilitate referrals to community-based social services organizations, the authors suggest that these platforms could also be useful in answering questions on the influence of past referrals on hospital costs, health quality outcomes, or readmission rates.

Finally, they note that there is an urgent need to develop assessment measures that can use unstructured data (like clinical notes or interpersonal communications between healthcare providers or providers and patient/family) to capture the dynamic aspects of healthcare data. “Having these measures would help us address research questions on whether the level of communication influences health care outcomes,” said Sinha.

The full study, “Putting Analytics into Action in Care Coordination Research: Emerging Issues and Potential Solutions,” Subodha Kumar, Liangfei Qiu, Arun Sen, and Atish Sinha, was published in Production and Operations Management, Volume 31, Issue 6, June 2022, pp. 2714-2738.

Research@Lubar Faculty scholarship in the Lubar College of Business spans the business fields and beyond through both theoretical and applied research that is published in leading journals.  Here are some of our faculty’s most recent publications:
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