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        “Big Data-based” Model for Hospital Admissions Predicts Impending Diseases and Better Estimates Hospital Stays

        December 2, 2021 By Michael Ares

        All News

        Individual Patient Data, Expansive Comorbidity Database Combine to Reveal Critical Patterns that Help Pre-empt Likely Hospital-borne Disease and Better Inform Initial Insurance Coverage

        A new model for predicting disease likelihood at the time of hospital admission can help healthcare providers generate a better estimate of a patient’s hospital stay and more effectively allocate scarce hospital resources. It can also provide insurance companies with a more realistic estimate of the eventual length of hospital stay for the patient, ensuring appropriate treatment is started earlier and resulting in better overall health outcomes.

        When will I get out of the hospitalThe research, entitled “When will I get out of the hospital? Modeling Length of Stay using Comorbidity Networks,” was co-authored by Pankush Kalgotra, Assistant Professor, Business Analytics in the Department of Systems and Technology at Auburn University’s Harbert College of Business, and Ramesh Sharda, Vice Dean, Watson Graduate School of Management and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. The peer-reviewed study was recently accepted for publication in the Journal of Management Information Systems, a top journal in the information systems discipline.

        The Challenge

        Business Analytics ModelingA key determinant of length of stay (LOS) when a patient enters a hospital is the level of information available to admissions staff at the time of intake – it defines the patient’s initial treatment plan as well as any insurance coverage originally authorized. But current intake processes typically consider only a small portion of critical patient information presented at admissions – current patient symptoms, their medical history (where available) and almost no information regarding how patients with similar symptoms end up progressing during their stays. This can be severely limiting, given that patients often develop new diseases during their stay in the hospital that expand the treatment needed and increase insurance coverage extensions required.

        But what if patterns of additional diseases acquired during hospital stays of similar patients could be collected and considered during the hospital admissions process? Could new disease expectations be accounted for, better treatment plans prescribed and more realistic cost estimates made right from the start?

        These are the questions Pankush and his co-author posed, and the answers they found present a host of opportunities for significant improvements in patient care, reductions in treatment costs and better allocations of scarce healthcare resources all around, particularly in COVID-19 times where ICU beds and critical equipment such as ventilators are in short supply.

        The Harbert College of Business sat down with Pankush to find out what prompted their research and what the healthcare industry needs to do to make practical, productive use of the valuable insight their study revealed.

        HCOB:    What prompted you to apply the power of Big Data and advanced business analytics techniques to this particular problem among the many that could have been chosen?
        Pankush:  The specific focus of this project came out of a personal experience I and the co-author had during the initial phases of this research, which was actually a part of my dissertation. My academic advisor had a friend who was admitted to a hospital for what was supposed to be a fairly routine surgery. As it turned out, this friend contracted pneumonia after the surgery, the pneumonia got complicated and he died in the hospital. We discovered that this was far from unusual – hospital patients often contract diseases that heretofore had been considered unrelated to the reason they were admitted into the hospital in the first place.
        HCOB:    We’ve all heard that hospitals can be breeding grounds for disease, and you’ve discovered hard data to support that view – with data on millions of patients backing you up. But you went further in that you found patterns of specific diseases whose likelihood could be predicted at the time of admission using advanced business analytics, correct?
        Pankush:    That’s right. My advisor’s friend entered the hospital for a specific surgical procedure, but then new medical issues arose that weren’t considered at the time of admission which ultimately became life-determining. We were already working on a number of related healthcare issues and had published research on those, and this incident motivated us to apply advanced business analytics to addressing this increasingly important dilemma.
        HCOB:       Can you walk us through a typical hospital intake process – what’s done and what’s not done – and how your analysis of all potential factors you’ve identified can be leveraged to produce what are clearly beneficial outcomes of hospitals employing your model?

        Sure. So, when a patient goes to a hospital, he or she goes in with some primary symptoms, "I'm having headaches." We discovered from our assessment of more than 20 million hospital patient records that, over the course of their hospital stay, many patients contract multiple other diseases. We proposed that a robust network of historical diseases contracted in hospitals could help identify which diseases come together in patients admitted to hospitals. For example, diabetes and hypertension are strongly related to each other.

        We then compiled a large network database of more than 15,000 diseases – and then mapped which ones go together – using historical values. That was Step One.

        But the second step was to use a patient’s symptoms at the time of admission and find the probable comorbidities that are not diagnosed yet but may show up during the hospital stay. Let's say a new patient comes into the hospital with just a headache. We then access the network we created and found that if a patient comes in with a headache, he or she is likely to contract something else during their hospital stay. We are predicting, based on our database, what he or she might contract during their hospital stay – some other, allegedly unrelated, disease or diseases.

        Using these three types of data – from the past, the present and the future – we can produce a predictive model that will postulate what other diseases are likely to arise in that patient. This information can augment their initial treatment plan as early in the process as possible. That way, preventative measures can be taken before the expected disease presentation occurs. Not only does this benefit the patient, but it also shortens hospital stays and facilitates the allocation and management of critical hospital resources.


        So, there are really two primary benefits of your research – first, and perhaps foremost from a healthcare outcomes perspective – is the ability your model offers for predicting the onset of additional diseases that are likely to occur during a patient’s hospital stay before they present themselves. This enables healthcare workers to include these likely new diseases in their initial treatment plans and their initial authorization requests for insurance coverage. That’s the health outcomes benefit.

        But, as an expert in advanced business analytics, what additional benefits did you envision for the business side of the equation – the advantages your model presents in helping hospitals better manage beds, ICU resources and other practical allocations? And how does this more realistic insight into eventual treatment needs and associated costs benefit insurers?


        For the hospital administrator, it's very easy to use the model. They simply plug in the history of their patients and some basic demographics and they'll get the numbers they need based on the data we’ve compiled for millions of other patients in similar circumstances.

        For insurers, knowing the treatments a covered patient will eventually need at the beginning of their hospital stay rather than later on provides a valuable, realistic perspective that they can use to project the financial liabilities associated with each patient. Armed with the results that come from employing this new and efficient predictive model, insurers will be able to better plan for and accommodate the real-world costs of each patient admission.

        HCOB:  What do hospital administrators and insurance providers each need to do to realize the benefits of your model?

        For hospital administrators, a lot is already being done in terms of leveraging the value of the electronic medical records systems (EMRs) required to use the model. In many instances, administrators have, or can easily acquire, the medical records of incoming patients in electronic form, according to leaders of some of the largest EMR providers in the world. So, they have much of what they need for many patients right now.

        As for insurers, we made a point to speak with a few of the largest healthcare insurance companies in the country, who also welcomed and helped structure the research we conducted. From a business perspective, we would like to reach out to the practitioners to share the results our model generates to help them realize the full benefits of this transparency to their bottom line.