Study suggests new computer analytics may solve the hospital readmission puzzle

BALTIMORE, Md, June 5, 2019 -A University of Maryland School of Medicine study suggests that a novel machine learning model developed at the University of Maryland Medical System (UMMS), called the Baltimore score (B score), may help hospitals better predict which discharged patients are likely to be readmitted.

The research was led by Daniel Morgan, MD, MS, Associate Professor of Epidemiology and Public Health at the University of Maryland School of Medicine (UMSOM). Dr. Morgan analyzed data on more than 14,000 patients from three UMMS hospitals using this special predictive score to determine the likelihood these patients would be readmitted.

The research, published in the journal JAMA Network Open, could help set the stage toward improving patient care and avoiding returns to the hospital.

“A significant proportion of readmissions may be preventable with better planning and follow-up for how the patient would transition back into the community,” said Dr. Morgan.

Readmissions occur for almost 20 percent of patients hospitalized in the United States and are associated with patient harm and expenses. Furthermore, rates of unplanned readmission within 30 days after discharge are used to benchmark a hospital’s performance and quality of patient care. Nevertheless, studies have shown that clinicians are poorly equipped to identify patients who will be readmitted, and many readmissions are thought to be preventable.

“If hospitals can better target time and money in planning for discharge to home, then patients may not have to come back to the hospital, with the harm sometimes associated with hospitals, including risks for infection, falls, delirium and other adverse events,” said Dr. Morgan.

Using Health Data and An Algorithm

Machine learning is widely used to make predictions about the future, based on a set of computer algorithms that analyze massive amounts of data. The algorithms form what is known as a neural network, modeled loosely after the human brain, to recognize and learn from patterns. In the realm of hospital patient care, the increased adoption of electronic health records makes it possible to apply machine learning techniques to health care data.

Existing readmission risk-assessment tools, including the LACE index, the HOSPITAL score and the Maxim/RightCare score, look at a limited set of variables for each patient, such as length of stay in a hospital, type and severity of admission, types and amounts of medications, other chronic conditions a patient may have, and previous hospital admissions.

One of the study’s co-authors, William Bame, a Senior Data Scientist at UMMS, designed a neural network to mine thousands of health data variables in real time. The system then calculated a score to predict a patient’s chance of returning after hospital discharge.

This experimental B score algorithm was individualized for each of three University of Maryland Medical System hospitals in different settings, after initially evaluating more than 8,000 possible data variables from September 1, 2014 through August 31, 2016. The final machine learning model drew from 382 variables, including demographics; lab test results; whether the patient required breathing assistance; body mass index; affiliation with a specific church; marital status; employment; medication usage and substance abuse.

Morgan and colleagues compared the B score readmission risk ranking to actual readmissions at the three hospitals, and to the predictions scored by the other programs. Across the three hospitals, despite the different settings, the B score overall was better able to identify patients at risk of readmission than other scores. It was most accurate among patients at highest risk. Patients scoring in the top 10 percent of B score risk at discharge had a 37.5 percent chance of 30-day unplanned readmission. Likewise, a patient in the top five percent B score at discharge had a 43.1 percent change of readmission.

“The widespread use of electronic health records has enhanced information flow from all clinicians involved in a patient’s treatment,” said UMSOM Dean E. Albert Reece, MD, PhD, MBA, University Executive Vice President for Medical Affairs and the John Z. and Akiko K. Bowers Distinguished Professor. “This study underscores how patient data may also help solve the readmission puzzle and, ultimately, improve the quality of patient care.”

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Morgan DJ, Bame B, Zimand P, Dooley P, Thom KA, Harris AD, Bentzen S, Ettinger W, Garrett-Ray SD, Tracy JK, Liang Y. “Assessment of Machine Leaning vs Standard Prediction Rules for Predicting Hospital Readmissions.” JAMA Network Open. 2019;2(3):e190348. doi: 10.1001/jamanetworkopen.2019.0348

Now in its third century, the University of Maryland School of Medicine was chartered in 1807 as the first public medical school in the United States. It continues today as one of the fastest growing, top-tier biomedical research enterprises in the world — with 43 academic departments, centers, insti-tutes, and programs; and a faculty of more than 3,000 physicians, scientists, and allied health pro-fessionals, including members of the National Academy of Medicine and the National Academy of Sciences, and a distinguished recipient of the Albert E. Lasker Award in Medical Research. With an operating budget of more than $1 billion, the School of Medicine works closely in partnership with the University of Maryland Medical Center and Medical System to provide research-intensive, academic and clinically based care for more than 1.2 million patients each year. The School has over 2,500 students, residents, and fellows, and more than $530 million in extramural funding, with most of its academic departments highly ranked among all medical schools in the nation in research funding. As one of the seven professional schools that make up the University of Maryland, Balti-more campus, the School of Medicine has a total workforce of nearly 7,000 individuals. The com-bined School and Medical System (“University of Maryland Medicine”) has an annual budget of nearly $6 billion and an economic impact more than $15 billion on the state and local community. The School of Medicine faculty, which ranks as the 8th highest among public medical schools in research productivity, is an innovator in translational medicine, with 600 active patents and 24 start-up companies. The School works locally, nationally, and globally, with research and treatment fa-cilities in 36 countries around the world. Visit medschool.umaryland.edu.

The University of Maryland Medical System (UMMS) is a university-based regional health care sys-tem focused on serving the health care needs of Maryland, bringing innovation, discovery and re-search to the care we provide and educating the state’s future physician and health care profession-als through our partnership with the University of Maryland School of Medicine and the UM Schools of Nursing, Pharmacy, Social Work and Dentistry in Baltimore. As one of the largest pri-vate employers in the State, the health system’s 28,000 employees and 4,000 affiliated physicians provide primary and specialty care in more than 150 locations and at 13 hospitals. UMMS’ flagship academic campus, the University of Maryland Medical Center in Baltimore is partnered with the University of Maryland School of Medicine and is recognized regionally and nationally for excel-lence and innovation in specialized care. Our acute care and specialty rehabilitation hospitals serve urban, suburban and rural communities and are located in 13 counties across the State. In addition, UMMS operates health insurance plans serving Medicare and Medicaid members. For more information, visit http://www.umms.org.