Decision Support for Healthcare Processes and Services
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ItemTowards a Model for Inclusive Healthcare Access post COVID-19( 2022-01-04)The devastation caused by the COVID-19 pandemic has exposed years of cyclic inequalities faced by disadvantaged and minority communities. Unequal access to healthcare and a lack of financial resources further exacerbates their suffering, especially during a pandemic. In such critical conditions, information technology-based healthcare services can be an efficient way of increasing access to healthcare for these communities. In this paper, we put forward a decision model for guiding the distribution of IT-based healthcare services for racial minorities. We augment the Health Belief Model by adding financial and technology beliefs. We posit that financial inclusion of minority populations increases their ability to access technology and, by extension, IT-based healthcare services. Financial inclusion and the use of secure private technologies like federated learning can indeed enable greater access to healthcare services for minorities. Therefore, we incorporate financial, health, and technology tools to develop a model for equitable delivery of healthcare services and test its applicability in different use-case scenarios.
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ItemShow Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data( 2022-01-04)Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection.
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ItemProcessing Patient Information Leaflets with Embeddings( 2022-01-04)As of 2021, more than 100,000 drugs are approved in Germany, 35,000 of which are non-prescriptive over-the-counter drugs. While proven information from medical studies is given in patient information leaflets, patients are often lost when trying to determine which drugs are compatible with their needs or which alternatives are suitable. We show that representing patient information leaflets as dense vectors allows us to extract more valuable medical information than is explicitly stated in the leaflets. Without any explicit insertion of medical knowledge, our embeddings capture concepts of generics, even with respect to the dosage form. Furthermore, the embeddings allow patients to identify drug clusters based on their treatment area and offer suitable alternatives based on analogical reasoning. The carved-out information may not only help patients to explore alternative drugs but also serve pharmacists and patients as a new way to search for drugs tailored to dietary, allergic, or medical needs.
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ItemOn Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems( 2022-01-04)Human-in-the-loop intelligence augmentation (IA) methods combined with visual analytics (VA) have the potential to provide additional functional capability and cognitively driven interpretability to Decision Support Systems (DSS) for health risk assessment and patient-clinician shared decision making. This paper presents some key ideas underlying the synthesis of IA with VA (IA/VA) and the challenges in the design, implementation, and use of IA/VA-enabled clinical decision support systems (CDSS) in the practice of medicine through data driven analytical models. An illustrative IA/VA solution provides a visualization of the distribution of health risk, and the impact of various parameters on the assessment, at the population and individual levels. It also allows the clinician to ask “what-if” questions using interactive visualizations that change actionable risk factors of the patient and visually assess their impact. This approach holds promise in enhancing decision support systems design, deployment and use outside the medical sphere as well.
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ItemMonte-Carlo Simulation Based on Patient-Individual Distributions for Supporting Intensive Care Occupancy Management( 2022-01-04)Managing Intensive Care Units (ICUs) in hospitals is a highly challenging endeavor. In particular, decisions such as admitting elective patients and discharging patients from the ICU have to be taken under a high level of uncertainty since the occupancy of ICUs does not only depend on these decisions but also on unknown parameters such emergency patient arrivals and lengths of stay of the patients in the ICU. In this paper, we develop a framework for supporting ICU occupation management by quantifying the impact of admission and discharge decisions on the probability of reaching critical ICU occupancy levels in a given planning horizon. A key component of this framework is the use of data-driven approaches for obtaining probability distributions for the parameters affected by uncertainty. In particular, we use standardized treatment and patient health state data to create patient-specific length-of-stay distributions with a Machine Learning approach. These patient-individual distributions are then validated and/or adjusted by medical experts. The validated distributions form the input to a Monte-Carlo Simulation that is used to approximate the probability distributions of the daily ICU occupancy levels resulting from ICU admission and discharge decisions. We experimentally evaluate our framework in a counterfactual simulation based on one year of historical data from 2019 from a medium-sized ICU in a German hospital. In that evaluation, we use a simple ICU management policy based on the probabilistic occupancy forecasts aiming at reducing the risk of running out of ICU capacity. The results show that following this policy would have avoided hitting critical occupancy levels by around 70% and would have had a smoothing effect on ICU occupancy levels.
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ItemModelling the Accessibility of Adult Psychology Services Using Discrete Event Simulation( 2022-01-04)With a growing number of people seeking treatment for mental health problems, mental health services are consequently coming under increased pressure resulting in longer waiting times and worsening of mental health problems. Service underfunding, overworked staff, and the looming threat of further demand due to the COVID-19 pandemic only add to the concerns. Hence it is imperative the efficiencies of these services are maximised to allow better access to quality treatment. We created a Discrete Event Simulation model to replicate the current clinical approach taken in an adult psychology clinic in the U.K.'s National Health Service. The model identifies bottlenecks in the service, and provides results on how different staffing scenarios could alleviate challenges.
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ItemManaging hospital visitor admission during Covid-19: A discrete-event simulation by the data of a German University Hospital( 2022-01-04)The Corona pandemic and the associated need for visitor restrictions have defined an entirely new management task in hospitals: The hospital visitor management. The admission process of hospital visitors and the implementation of associated infection-prevention strategies such as the delivery of face masks thereby pose major challenges. In this work, we evaluate both implemented and planned admission processes in a German University Hospital based on a discrete-event simulation model and provide distinct recommendations for hospital visitor management with special consideration of digitalization, antigen testing, waiting times, space and staff utilization. We find the extraordinary potential of digitalization with a reduction of visitor waiting and service times of up to 90 percent, the significant burden for personnel and room capacity, in terms of antigen testing, especially, and the need for visitor restrictions in terms of a maximum number of visitors per inpatient.
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ItemIntra-day Dynamic Rescheduling under Patient No-shows( 2022-01-04)Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intra-day dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that can solve for the optimal policy more efficiently. Based on a numerical study, we estimate that using intra-day dynamic rescheduling can lead to a 5-7% decrease in expected cost when compared to static scheduling.
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ItemExtended Length of Hospital Stay for Surgical and Medical Patients – Insights from Hospital and Psychosocial Predictors( 2022-01-04)Ensuring that patients do not overstay the expected Length of Stay (LOS) in the hospital is a recognized indicator of the quality of care received and helps to reduce the cost of healthcare. Moreover, it is a key factor in value-based care models. This study identifies the predictors of Extended Length of Hospital Stay (ELOHS) for surgical and medical patients to include LOS (>20 days), Age (> 40 years), Hour to Surgery (HTS) – within 4 hours of admission, zero and one Rapid Response Team (RRT) calls, Average Operating Room Time (AORT) of 0 – 120 minutes and one Theatre Session (TS). Apart from the “ear, nose, mouth & throat”, “kidney and urinary tract”, “circulatory system”, “nervous system” and “digestive system” Major Diagnostic Categories (MDCs), other considered MDCs have significant differences in the Classification of Hospital Acquired Diagnoses (CHAD) rate of ELOHS and Normal Length of Hospital Stay (NLOHS) patients. It is expected that the early consideration of ELOHS predictors will be vital in improving patients’ outcomes in the hospital.
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