7 research outputs found
Surgery scheduling heuristic considering OR downstream and upstream facilities and resources
Background: Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists). Methods: Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries’ completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling. Results: Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries’ completions by 55.5%. A more uniform distribution of patients’ arrivals at the PACU was also observed. Conclusions: Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation
Man vs. machine : predicting hospital bed demand
Background: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems. Objective: Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED. Methods: This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC). Results: All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77–0.87], 0.80 (95% CI: 0.75–0.85), 0.76 (95% CI: 0.71–0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR. Conclusions: Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task
Applications of Text Mining Techniques in Operations Management
A presente tese apresenta proposições para o desenvolvimento e aplicação de tĂ©cnicas de mineração de textos, de modo a contribuir para a gestĂŁo de operações nas áreas mĂ©dicas e de negĂłcios. Os objetivos desta tese sĂŁo: (i) identificar e estruturar tĂ©cnicas de mineração de texto, de modo a elaborar um mĂ©todo para prever internações de pacientes provenientes de emergĂŞncias hospitalares, tendo como base somente os registros textuais nĂŁo estruturados escritos por mĂ©dicos durante o primeiro encontro mĂ©dico-paciente; (ii) comparar previsões realizadas pelo mĂ©todo proposto no objetivo (i) com análises mĂ©dicas realizadas por humanos, de modo a verificar se computadores podem atuar de forma autĂ´noma na tarefa de previsĂŁo de internações de pacientes provenientes de emergĂŞncias hospitalares; e (iii) identificar e estruturar tĂ©cnicas de mineração de texto, de modo a elaborar um mĂ©todo para prever a satisfação de clientes de companhias aĂ©reas, tendo como base as avaliações escritas e publicadas por passageiros na internet. Os mĂ©todos propostos utilizaram diferentes tĂ©cnicas de mineração de textos, sendo validados por estudos de caso. Em relação à área mĂ©dica, o mĂ©todo proposto pode realizar previsões em tempo real sobre a necessidade de leitos, ajudando as equipes de gerenciamento de leitos a melhorar os processos de fluxo de pacientes. AlĂ©m disso, verificou-se que tanto mĂ©dicos (iniciantes ou experientes), quanto máquina, tiveram desempenhos semelhantes na tarefa de previsĂŁo de internação de pacientes. Já em relação à área de negĂłcios, o mĂ©todo proposto permitiu extrair dimensões de satisfação de avaliações online, alĂ©m dos sentimentos associados a elas, considerando diferentes perfis de passageiros, serviços e perĂodos de tempo. Desta forma, foi possĂvel prever a recomendação de companhias aĂ©reas baseado nas avaliações escritas por passageiros.This dissertation presents propositions for the development and application of text mining techniques, in order to contribute to operations management in the medical and business areas. The objectives of this dissertation are: (i) identify and structure text mining techniques, in order to propose a method to predict admissions of patients from hospital emergencies, based only on unstructured textual records written by physicians during the first encounter with patients; (ii) compare predictions made by the method proposed in objective (i) with medical analyses carried out by humans, in order to verify if computers can work autonomously in predicting hospitalizations of patients coming from hospital emergencies; and (iii) identify and structure text mining techniques to develop a method for predicting airline customer satisfaction based on online customer reviews. The proposed methods used different text mining techniques, being validated by case studies. Regarding the medical area, the proposed method was able to perform real-time forecasts about the need for beds, helping bed management teams to improve patient flow processes. In addition, it was found that both physicians (novice or experienced) and machine had similar performances in predicting patient hospitalization. In relation to the business area, the proposed method allowed to extract satisfaction dimensions of online customer reviews, as well as sentiments associated to them, considering different profiles of passengers, services and time periods. It also enabled the prediction of airline recommendation based on online customer reviews
MĂ©todo para identificação de quedas de consumo atĂpicas em unidades consumidoras de energia elĂ©trica
Esse trabalho tem por objetivo aprimorar a principal atividade das empresas de distribuição no que diz respeito ao combate Ă s perdas comerciais: as inspeções em campo Ă s unidades consumidoras (UCs). Para tanto, Ă© feita a proposição de um mĂ©todo para identificar quedas de consumo atĂpicas dentro do universo de faturamento de UCs de uma concessionária de energia elĂ©trica. A proposta está fundamentada na análise dos registros histĂłricos de consumo, de modo que os dados considerados atĂpicos possam ser indicados e as UCs ranqueadas de acordo com a prioridade para as inspeções em campo. Para tanto, propõe-se a utilização combinada de tĂ©cnicas de previsĂŁo de demanda e de estatĂsticas robustas. A validade do mĂ©todo foi verificada atravĂ©s de um estudo de caso em uma empresa de distribuição de energia elĂ©trica do sul do Brasil. AtravĂ©s do estudo de caso, concluiu-se que o mĂ©todo Ă© capaz de identificar quedas de consumo atĂpicas, tendo identificado satisfatoriamente 89,38% dos casos avaliados. Ao final do trabalho, sĂŁo apresentadas sugestões de estudos complementares, de modo a aperfeiçoar o desempenho do mĂ©todo.This study aims to enhance the main business of distribution companies regarding to the efforts to avoid non-technical losses, that means, field inspections at the consumer units (CUs). For that, the proposition of an algorithm to identify atypical consumption falls within the universe of PAs billing of an electric facility is made. The proposal is based on the analysis of historical records of consumption, so that the data which are considered atypical can be indicated and the CUs ranked according to their priority for inspections in the field. Combined techniques of demand forecasting and statistics robust are proposed. The validity of the algorithm was verified through a case study in an electric power distribution facility in southern Brazil. Through the case study, it was concluded that the algorithm is able to identify atypical consumption falls, and satisfactorily 89.38% of the cases was identified. At the end of this paper, suggestions for further studies in order to improve the performance of the algorithm are presented
Forecasting daily volume and acuity of patients in the emergency department
This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Cl´ınicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days.The demand time serieswas stratified according to patient classification using the Manchester Triage System’s (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification
Man vs. machine: Predicting hospital bed demand from an emergency department.
BackgroundThe recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems.ObjectiveCompare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED.MethodsThis was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC).ResultsAll graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77-0.87], 0.80 (95% CI: 0.75-0.85), 0.76 (95% CI: 0.71-0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR.ConclusionsOur data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task