Predicting the future: introducing business analytics to endoscopy units

Background and aims: currently, most endoscopy software only provides limited statistics of past procedures, while none allows patterns to be extrapolated. To overcome this need, the authors applied business analytic models to predict future demand and the need for endoscopists in a tertiary hospital Endoscopy Unit. Methods: a query to the endoscopy database was performed to retrieve demand from 2015 to 2021. The graphical inspection allowed inferring of trends and seasonality, perceiving the impact of the COVID-19 pandemic, and selecting the best forecasting models. Considering COVID-19’s impact in the second quarter of 2020, data for esophagogastroduodenoscopy (EGD) and colonoscopy was estimated using linear regression of historical data. The actual demand in the first two quarters of 2022 was used to validate the models. Results: during the study period, 53,886 procedures were requested. The best forecasting models were: a) simple seasonal exponential smoothing for EGD, colonoscopy and percutaneous endoscopic gastrostomy (PEG); b) double exponential smoothing for capsule endoscopy and deep enteroscopy; and c) simple exponential smoothing for endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS). The mean average percentage error ranged from 6.1 % (EGD) to 33.5 % (deep enteroscopy). Overall, 8,788 procedures were predicted for 2022. The actual demand in the first two quarters of 2022 was within the predicted range. Considering the usual time allocation for each technique, 3.2 full-time equivalent endoscopists (40 hours-dedication to endoscopy) will be required to perform all procedures in 2022. Conclusions: the incorporation of business analytics into the endoscopy software and clinical practice may enhance resource allocation, improving patient-focused decision-making and healthcare quality (AU)

A labor requirements function for sizing the health workforce.

BACKGROUND: Ensuring healthcare delivery is dependent both on the prediction of the future demand for healthcare services and on the estimation and planning for the Health Human Resources needed to properly deliver these services. Although the Health Human Resources planning is a fascinating and widely researched topic, and despite the number of methodologies that have been used, no consensus on the best way of planning the future workforce requirements has been reported in the literature. This paper aims to contribute to the extension and diversity of the range of available methods to forecast the demand for Health Human Resources and assist in tackling the challenge of translating healthcare services to workforce requirements. METHODS: A method to empirically quantify the relation between healthcare services and Health Human Resources requirements is proposed. For each one of the three groups of specialties identified-Surgical specialties, Medical specialties and Diagnostic specialties (e.g., pathologists)-a Labor Requirements Function relating the number of physicians with a set of specialty-specific workload and capital variables is developed. This approach, which assumes that health managers and decision-makers control the labor levels more easily than they control the amount of healthcare services demanded, is then applied to a panel dataset comprising information on 142 public hospitals, during a 12-year period. RESULTS: This method provides interesting insights on healthcare services delivery: the number of physicians required to meet expected variations in the demand for healthcare, the effect of the technological progress on healthcare services delivery, the time spent on each type of care, the impact of Human Resources concentration on productivity, and the possible resource allocations given the opportunity cost of the physicians' labor. CONCLUSIONS: The empirical method proposed is simple and flexible and produces statistically strong models to estimate Health Human Resources requirements. Moreover, it can enable a more informed allocation of the available resources and help to achieve a more efficient delivery of healthcare services.

Forecasting the medical workforce: a stochastic agent-based simulation approach.

Starting in the 50s, healthcare workforce planning became a major concern for researchers and policy makers, since an imbalance of health professionals may create a serious insufficiency in the health system, and eventually lead to avoidable patient deaths. As such, methodologies and techniques have evolved significantly throughout the years, and simulation, in particular system dynamics, has been used broadly. However, tools such as stochastic agent-based simulation offer additional advantages for conducting forecasts, making it straightforward to incorporate microeconomic foundations and behavior rules into the agents. Surprisingly, we found no application of agent-based simulation to healthcare workforce planning above the hospital level. In this paper we develop a stochastic agent-based simulation model to forecast the supply of physicians and apply it to the Portuguese physician workforce. Moreover, we study the effect of variability in key input parameters using Monte Carlo simulation, concluding that small deviations in emigration or dropout rates may originate disparate forecasts. We also present different scenarios reflecting opposing policy directions and quantify their effect using the model. Finally, we perform an analysis of the impact of existing demographic projections on the demand for healthcare services. Results suggest that despite a declining population there may not be enough physicians to deliver all the care an ageing population may require. Such conclusion challenges anecdotal evidence of a surplus of physicians, supported mainly by the observation that Portugal has more physicians than the EU average.

Handling healthcare workforce planning with care: where do we stand?

BACKGROUND: Planning the health-care workforce required to meet the health needs of the population, while providing service levels that maximize the outcome and minimize the financial costs, is a complex task. The problem can be described as assessing the right number of people with the right skills in the right place at the right time, to provide the right services to the right people. The literature available on the subject is vast but sparse, with no consensus established on a definite methodology and technique, making it difficult for the analyst or policy maker to adopt the recent developments or for the academic researcher to improve such a critical field. METHODS: We revisited more than 60 years of documented research to better understand the chronological and historical evolution of the area and the methodologies that have stood the test of time. The literature review was conducted in electronic publication databases and focuses on conceptual methodologies rather than techniques. RESULTS: Four different and widely used approaches were found within the scope of supply and three within demand. We elaborated a map systematizing advantages, limitations and assumptions. Moreover, we provide a list of the data requirements necessary to implement each of the methodologies. We have also identified past and current trends in the field and elaborated a proposal on how to integrate the different methodologies. CONCLUSION: Methodologies abound, but there is still no definite approach to address HHR planning. Recent literature suggests that an integrated approach is the way to solve such a complex problem, as it combines elements both from supply and demand, and more effort should be put in improving that proposal.

An optimization based on simulation approach to the patient admission scheduling problem: diagnostic imaging department case study.

The growing influx of patients in healthcare providers is the result of an aging population and emerging self-consciousness about health. In order to guarantee the welfare of all the healthcare stakeholders, it is mandatory to implement methodologies that optimize the healthcare providers' efficiency while increasing patient throughput and reducing patient's total waiting time. This paper presents a case study of a conventional radiology workflow analysis in a Portuguese healthcare provider. Modeling tools were applied to define the existing workflow. Re-engineered workflows were analyzed using the developed simulation tool. The integration of modeling and simulation tools allowed the identification of system bottlenecks. The new workflow of an imaging department entails a reduction of 41 % of the total completion time.

Quality of life in asthmatic outpatients.

Asthma patients incur a great cost in terms of loss of quality of life. The purpose of this study is to evaluate the relative contribution and relationship of several patient- and disease-related factors, measured by several variables, to the quality of life in adults with asthma. Two hundred and ten asthmatic outpatients over 18 years old, registered in a Family Health Unit, were randomly selected to complete the Asthma Quality of Life (AQLQ) and Short Form Generic questionnaires (SF-36), respectively. Single and multiple linear regression models were developed to explain the variability of the summary scores of AQLQ and Physical and Mental Health SF-36. As potential predictors, the following independent variables were used: gender, age, number of comorbidities, asthma severity following the Global Initiative for Asthma (GINA) criteria, asthma control (measured by ACQ questionnaire), %FEV1 (forced expiratory volume in the first second) and, for the first time, Graffar Score to assess socioeconomical features. The Graffar Score is an index that divides the population in 5 socioeconomic layers. We report the best Adjusted R Square of these models published in the literature, ranging from 0.40 to 0.76. Women showed poorer quality of life than men. The best predictor of AQLQ was ACQ, followed by Asthma Severity, Gender and %FEV1. The best predictors of Physical and Mental Health SF-36 were, by decreasing importance, ACQ, number of comorbidities, Gender and Graffar Score. We note that the variable number of comorbidities was included in both SF-36 models, but not in AQLQ model. Asthma Severity and %FEV1 did not enter into SF-36 models. We conclude that besides clinical and functional measures, the evaluation process of the overall health status must incorporate quality-of-life measures.