A Machine Learning Approach for Predicting Inpatient Discharge at Central Maine Medical Center

Operating with a finite quantity of beds, medical resources, and physicians, hospitals are constantly allocating resources under conditions of scarcity. Misallocation of resources and operational inefficiencies are a substantial driver of the United States’ strikingly high healthcare costs. Accurately forecasting the duration which a specific patient will stay in a hospital, also known as a patient’s length of stay, could assist hospital decision makers in optimizing their workflow and allocating their resources efficiently. This paper demonstrates the superiority of a survival random forest approach over classical econometric techniques and current practice at the Central Maine Medical Center. Included in the discussion is an assessment of the strengths and weaknesses of the model, with the hope of informing the application of machine learning methods in the real world

Demand and Capacity Modelling of Acute Services Using Simulation and Optimization Techniques

The level of difficulty that hospital management have been experiencing over the past decade in terms of balancing demand and capacity needs has been at an unprecedented level in the UK. Due to shortage of capacity, hospitals are unable to treat patients, and in some cases, patients are transferred to other hospitals, outpatient referrals are delayed, and accident and emergency (A&E) waiting times are prolonged. So, it’s time to do things differently, because the current status quo is not an option. A whole hospital level decision support system (DSS) was developed to assess and respond to the needs of local populations. The model integrates every component of a hospital (including A&E, all outpatient and inpatient specialties) to aid with efficient and effective use of scarce resources. An individual service or a specialty cannot be assumed to be independent, they are all interconnected. It is clear from the literature that this level of generic hospital simulation model has never been developed before (so this is an innovative DSS). Using the Hospital Episode Statistics and local datasets, 768 forecasting models for the 28 outpatient and inpatient specialties are developed (to capture demand). Within this context, a variety of forecasting models (i.e. ARIMA, exponential smoothing, stepwise linear regression and STLF) for each specialty of outpatient and inpatient including the A&E department were developed. The best forecasting methods and periods were selected by comparing 4 forecasting methods and 3 periods (i.e. daily, weekly and monthly) according to forecast accuracy values calculated by the mean absolute scaled error (MASE). Demand forecasts were then used as an input into the simulation model for the entire hospital (all specialties). The generic hospital simulation model was developed by taking into account all specialties and interactions amongst the A&E, outpatient and inpatient specialties. Six hundred observed frequency distributions were established for the simulation model. All distributions used in the model were based on age groups. Using other inputs (i.e. financial inputs, number of follow ups, etc.), the hospital was therefore modelled to measure key output metrics in strategic planning. This decision support system eliminates the deficiencies of the current and past studies around modelling hospitals within a single framework. A new output metric which is called ‘demand coverage ratio’ was developed to measure the percentage of patients who are admitted and discharged with available resources of the associated specialty. In addition, a full factorial experimental design with 4 factors (A&E, elective and non-elective admissions and outpatient attendance) at 2 levels (possible 5% and 10% demand increases) was carried out in order to investigate the effects of demand increases on the key outputs (i.e. demand coverage ratio, bed occupancy rate and total revenue). As a result, each factor is found to affect total revenue, as well as the interaction between elective and non-elective admissions. The demand coverage ratio is affected by the changes in outpatient demands as well as A&E arrivals and non-elective admissions. In addition, the A&E arrivals, non-elective admissions and elective admissions are most important for bed occupancy rates, respectively. After an exhaustive review of the literature we notice that an entire hospital model has never been developed that combines forecasting, simulation and optimization techniques. A linear optimization model was developed to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from forecasting and forecasting-simulation) for each inpatient elective and non-elective specialty. In conclusion, these results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans. This hospital decision support system can become a crucial instrument for decision makers for efficient service in hospitals in England and other parts of the world

A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

© 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an accepted manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 03 Feb 2020, available online: https://doi.org/10.1080/01605682.2019.1700186.The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, (1) forecasting demand for all the specialties, (2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and (3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.Peer reviewe

Facilitating Patient‐Centric Thinking in Hospital Facility Management: A Case of Pharmaceutical Inventory

Conventional hospital facility management (FM) focuses on reasonably allocating various resources to support core healthcare services from the perspectives of the FM department and hos-pital. However, since patients are the main service targets of hospitals, the patients’ demographic and hospitalization information can be integrated to support the patient‐centric facility manage-ment, aiming at a higher level of patient satisfaction with respect to the hospital environment and services. Taking the pharmaceutical services in hospital inpatient departments as the case, forecasting the pharmaceutical demands based on the admitted patients’ information contributes to not only better logistics management and cost containment, but also to securing the medical require-ments of individual patients. In patient‐centric facility management, the pharmacy inventory is re-garded as the combination of medical resources that are reserved and allocated to each admitted patient. Two forecasting models are trained to predict the inpatients’ total medical requirement at the beginning of the hospitalization and rectify the patients’ length of stay after early treatment. Specifically, once a patient is admitted to the hospital, certain amounts of medical resources are reserved, according to the inpatient’s gender, age, diagnosis, and their preliminary expected days in the hospital. The allocated inventory is updated after the early treatment by rectifying the inpa-tient’s estimated length of stay. The proposed procedure is validated using medical data from eight-een hospitals in a Chinese city. This study facilitates the integration of patient‐related information with the conventional FM processes and demonstrates the potential improvement in patients’ satisfaction with better hospital logistics and pharmaceutical services

Predicting inpatient flow at a major hospital using interpretable analytics

Problem definition: Turn raw data from Electronic Health Records into accurate predictions on patient flows and inform daily decision-making at a major hospital. Practical Relevance: In a hospital environment under increasing financial and operational stress, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63; 432 admissions at a large academic hospital (50.0% female, median age 64 years old, median length-of-stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial Implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques which is equally accurate, interpretable, frugal in data and computational power, and production-ready

Heuristiken im Service Operations Management

This doctoral thesis deals with the application of operation research methods in practice. With two cooperation companies from the service sector (retailing and healthcare), three practice-relevant decision problems are jointly elicited and defined. Subsequently, the planning problems are transferred into mathematical problems and solved with the help of optimal and/or heuristic methods. The status quo of the companies could be significantly improved for all the problems dealt with.Diese Doktorarbeit beschäftigt sich mit der Anwendung von Operation Research Methoden in der Praxis. Mit zwei Kooperationsunternehmen aus dem Dienstleistungssektor (Einzelhandel und Gesundheitswesen) werden drei praxisrelevante Planungsprobleme gemeinsam eruiert und definiert. In weiterer Folge werden die Entscheidungsmodelle in mathematische Probleme transferiert und mit Hilfe von optimalen und/oder heuristischen Verfahren gelöst. Bei allen behandelten Problemstellungen konnte der bei den Unternehmen angetroffene Status Quo signifikant verbessert werden

Hospitalizations due to Diabetes in Portugal: a time series analysis

Trabalho de projeto de mestrado em Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2020A diabetes é uma doença metabólica multifatorial, caracterizada por níveis elevados de glucose no sangue (hiperglicemia). Esta é uma condição crónica, resultante da progressiva destruição ou disfunção das células beta presentes no pâncreas, onde se dá a produção de insulina, uma hormona anabólica envolvida na absorção e metabolismo da glucose. Sintomas clássicos de diabetes incluem fome e sede excessivas (polifagia e polidipsia, respetivamente), vontade frequente de urinar (poliúria) e cansaço. Se o nível de glucose no sangue estiver continuamente acima do normal, podem ocorrer manifestações mais graves da doença, como sejam cetoacidose e coma hiperosmolar, com perigo de morte associado. A longo-prazo, indivíduos com diabetes têm um risco aumentado de complicações micro e macro vasculares, tais como retinopatia, nefropatia, neuropatia e doenças cardiovasculares. O controlo da doença é feito através de tratamento farmacológico, aliado a um estilo de vida saudável, procurando evitar ou retardar, tanto quanto possível, consequências graves. No entanto, uma baixa-autopercepção do risco de complicações e falhas no acompanhamento destes doentes ao nível dos cuidados de saúde primários contribuem para piores resultados clínicos, com eventual necessidade de cuidados de saúde hospitalares. O presente trabalho teve por objetivo descrever e modelar uma série temporal de internamentos hospitalares por diabetes em Portugal, com ênfase na predição. Para tal, foram usados dados constantes da Base de dados de Morbilidade Hospitalar, cedida pela Administração Central do Sistema de Saúde (ACSS), I.P., do Ministério da Saúde. Foram selecionados todos os diagnósticos de diabetes como causa primária de admissão, codificados, até ao terceiro dígito, por 250 (diabetes mellitus), de acordo com a Classificação Internacional de Doenças (ICD), 9ª revisão, Modificação Clínica (ICD-9-CM) ou E10 (diabetes tipo 1), E11 (diabetes tipo 2), E13 (outro tipo de diabetes), segundo a 10ª revisão da ICD (ICD-10-CM/PCS). Cada um destes registos foi associado a um episódio específico, através de um número sequencial único entre bases de dados, selecionando-se aqueles com data de admissão entre 1 de janeiro de 2010 e 31 de dezembro de 2018 e internamento mínimo de um dia. Com base nestes dados, foi construída uma série temporal do número mensal internamentos por diabetes entre 2010 e 2018, num total de 108 observações. Um subconjunto destes dados, composto por observações entre janeiro de 2010 e dezembro de 2016, foi utilizado na identificação e estimação do modelo (conjunto de treino; 84 meses), e as restantes observações, entre janeiro de 2017 e dezembro de 2018, usadas exclusivamente para validação do modelo (conjunto de teste; 24 meses). Seguindo a metodologia de Box e Jenkins para modelos Autorregressivos e de Médias Móveis Integrados Sazonais (SARIMA), vários modelos foram identificados com base na análise gráfica das funções de autocorrelação e autocorrelação parcial e estimados por máxima verosimilhança. Na seleção do melhor modelo, foi considerado o critério de informação de Akaike (AIC), avaliando-se posteriormente a sua adequação aos dados através da estatística de Ljung-Box e inspeção visual dos resíduos. A capacidade preditiva do modelo selecionado foi investigada por comparação entre as previsões obtidas e os dados do conjunto de teste, através de um procedimento de avaliação de origem móvel, em que novos valores são imputados ao modelo à medida que, supostamente, se tornam conhecidos. Neste caso, considerou-se quer a atualização do modelo, quer a sua recalibração, tendo por base uma janela fixa, onde se incluem todas as observações disponíveis até ao momento, ou móvel, composta pelas 84 observações mais recentes. Medidas como o erro absoluto médio (MAE), a raiz do erro quadrático médio (RMSE) e o erro percentual absoluto médio (MAPE) foram utilizadas para quantificar a precisão do modelo em cada contexto de predição, permitindo a sua comparação com um método de referência, um passeio aleatório sazonal, segundo o qual cada previsão iguala o valor da série no mesmo mês do ano anterior. Entre janeiro de 2010 e dezembro de 2018, foram contabilizados em Portugal 73.050 episódios de internamento por diabetes (676 casos por mês, em média), o que representa 35% de todas as admissões hospitalares por esta causa. Este número resulta, na sua maioria, de admissões urgentes (79,5%). A distribuição por sexo mostrou-se relativamente equilibrada (52,5% de homens), tendo sido observado um maior número de internamentos entre indivíduos com idade igual ou superior a 60 anos e na região Norte do país. Globalmente, o número de episódios diminuiu 45% entre 2010 e 2018 (10.011 e 5.530 internamentos, respetivamente). Para além da tendência decrescente, foram também observadas flutuações sazonais, com um pico de casos nos meses de Inverno e números mais baixos no Verão. Tendo por base o número de internamentos por mês entre 2010 e 2016, foram identificados e estimados nove modelos candidatos para a série original e diferenciada, quer na componente regular, quer sazonal. Entre estes, o modelo mais parcimonioso, SARIMA(1, 1, 2) × (0, 1, 1)12 (AIC = 10,647), foi usado para prever o número mensal de internamentos em 2017 e 2018. Considerando a disponibilidade de novos dados a cada mês, foi avaliada a capacidade preditiva do modelo para os horizontes temporais de 1, 3, 6 e 12 meses. De uma forma geral, o modelo re-estimado teve um melhor desempenho do que o modelo atualizado, registando-se o menor erro médio em previsões a um mês obtidas por meio de um janela móvel (MAE = 39,5; RMSE = 47,4; MAPE = 7,8%). Independentemente de ser usada uma janela fixa ou móvel na recalibração do modelo, a capacidade preditiva deste piorou com o aumento do horizonte temporal para 3, 6 e 12 meses. Em todo o caso, quer por via da atualização, quer da recalibração do modelo, foi observado um erro relativo inferior a 10% num horizonte temporal até seis meses. Foi ainda calculado o MAPE para 2017 e 2018, considerando a re-estimação do modelo com janela móvel a cada 1, 3, 6 e 12 meses. Neste caso, previsões a três meses apresentaram a maior precisão, com um erro anual médio de 7,7%, muito próximo do obtido com previsões a um mês (MAPE = 7,8%). Da re-estimação do modelo a cada 12 meses resultou o maior erro de previsão (MAPE = 12,4%), representando, ainda assim, uma redução de 30% face ao modelo de referência (MAPE = 17,7%). A representação gráfica das previsões do modelo SARIMA mostrou que estas ficaram maioritariamente acima da série observada, sobretudo em 2018. Não obstante, com a exceção de fevereiro de 2017 e maio de 2018, todos os valores observados se situaram entre os limites obtidos para as previsões. Estes resultados suportam a aplicação de modelos SARIMA na previsão de internamentos por diabetes em Portugal a curto/médio prazo, permitindo que decisões ao nível da gestão hospitalar sejam tomadas atempadamente. Esta seria uma forma de melhorar a capacidade de resposta dos serviços de saúde, sobretudo em períodos de maior fluxo de pacientes. Permitiria, igualmente, um uso mais eficiente do orçamento, pela adequação de recursos às reais necessidades dos pacientes, sem comprometer a qualidade dos cuidados prestados. Não desvirtuando o estudo de um ponto de vista clínico e epidemiológico, este tem associadas algumas limitações metodológicas. Começar por referir que foram incluídos apenas os diagnósticos principais de diabetes e os tipos mais comuns da doença, subestimando quer o seu impacto, quer a procura de cuidados de saúde específicos por esta causa. Por outro lado, o uso de dados agregados a nível nacional inviabiliza o uso das previsões obtidas em contextos reais de prática clínica. No que concerne à modelação dos dados, o uso do AIC como critério de seleção pode ter levado a uma interpretação errónea da qualidade dos modelos, dada a sua aplicação a diferentes conjuntos de dados (série original e diferenciada). Como trabalho futuro neste campo de investigação, conta-se a realização de uma análise espaciotemporal de internamentos hospitalares por diabetes, segundo métodos Bayesianos hierárquicos de mapeamento de doenças, considerando características sociodemográficas da população e indicadores de acesso a cuidados de saúde a nível regional.Diabetes is a chronic disease characterized by high blood sugar levels, as a result of the progressive destruction or dysfunction of the pancreatic -cells that produce insulin, an anabolic hormone involved in cellular glucose uptake and metabolism. Poor self-management and inefficient monitoring at primary health care contribute to an inadequate glycaemic control, which often leads patients to seek hospital health care, while facing acute or long-term complications of diabetes. The objective of this study is then to describe and model a series of hospitalizations due to diabetes in Portugal, with an emphasis on prediction. Episodes of hospital admissions occurred between 2010 and 2018 with main diagnosis of diabetes, coded, up to the third digit, by 250, according to International Classification of Diseases (ICD), 9th Revision, Clinical Modification (ICD-9-CM), or E10, E11, E13, based on the 10th revision of ICD (ICD-10-CM/PCS), and duration of at least one day were selected from the Hospital Morbidity Databases provided by the Central Administration of the Health System (ACSS), I.P. Following the Box-Jenkins approach for Seasonal Autoregressive Integrated Moving Average (SARIMA) modelling, a time series analysis on monthly hospitalizations in Portugal from January 2010 to December 2018 was conducted. Using data from 2010 to 2016 (84 observations), several models were identified as suitable and estimated by maximum likelihood. Akaike´s information criterion (AIC) was used to select the best model, whose adequacy was further investigated by residual analysis. For the selected model, 1, 3, 6 and 12-month forecasts were computed and compared against the observed series in 2017 and 2018, based on rolling-origin-update and rolling-origin-recalibration evaluation, with either a fixed (all available data) or rolling window (data from the last 84 months). The predictive ability of this model was assessed using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE), and compared with a benchmark method, namely a Seasonal Random Walk. From 2010 to 2018, there were 73,050 hospitalizations due to diabetes in Portugal, representing 35% of all admissions for this cause. The series of monthly hospitalizations exhibits a decreasing trend and apparent seasonality, with a higher number of episodes observed in winter months. From nine candidate models, the SARIMA(1, 1, 2) × (0, 1, 1)12 (AIC = 10.647) was selected as the most parsimonious and used to predict hospitalizations in 2017 and 2018. For both rolling-origin-update and rolling-origin-recalibration, the relative error was lower than 10% for a forecast horizon up to six months. Overall, rolling-origin-recalibration performed better, with the lowest MAPE obtained with one-month forecasts, given either a fixed or a rolling window (8.2% and 7.8%, respectively). As the forecast timespan increased, up to 3, 6 and 12-months, the predictive accuracy of the model worsened. The average error for 2017 and 2018, obtained by using a rolling window to re-estimate the model every 1, 3, 6 and 12 months, revealed predictions at three months as the most accurate (MAPE = 7,7%), followed by those at one month (MAPE = 7,8%). The highest error was obtained with 12-month forecasts (MAPE = 12,4%), still representing a 30% reduction in relation to the benchmark model (MAPE = 17,7%). The graphical representation of the forecasts showed that the selected model often overestimated the observed series, yet, all but two observations were in the 95% prediction interval. The selected model was able to capture the seasonal patterns of the series, revealing a good predictive ability up to six months. These findings suggest that SARIMA models can be used to forecast hospitalizations due to diabetes at short/medium term with good accuracy, allowing for management decisions to be taken timely. Future work on this field of research includes a spatiotemporal analysis of hospitalizations due to diabetes in Portugal, following a Bayesian hierarchical disease mapping approach, while taking into account population socioeconomic characteristics and access to health care

Practical approach on frail older patients attended for acute heart failure

Acute heart failure (AHF) is a multi-organ dysfunction syndrome. In addition to known cardiac dysfunction, non-cardiac comorbidity, frailty and disability are independent risk factors of mortality, morbidity, cognitive and functional decline, and risk of institutionalization. Frailty, a treatable and potential reversible syndrome very common in older patients with AHF, increases the risk of disability and other adverse health outcomes. This position paper highlights the need to identify frailty in order to improve prognosis, the risk-benefits of invasive diagnostic and therapeutic procedures, and the definition of older-person-centered and integrated care plans