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The aim of this research is to develop a model to forecast short-term health cost changes. The motivation for producing such a model is to provide local decision makers with a tool to predict short-term health-care costs in their localities. In order to achieve this objective, we collected data on total health-care expenditures and demographic data for California counties from 2000 to 2007. We then used various statistical methods to better understand the data and developed a regression model. Each year's prediction model was then used to forecast the following year's total health-care expenditure. The model developed adequately predicted health-care costs for the years on which the model was developed (2000–2006), and adequately forecast health-care costs for the holdout year, 2007. The average adjusted R2 value was 0.57, with an average mean absolute deviation score of 34. The best predictors of total health-care expenditures were county population, the number of county health-care facilities, and county per capita personal income. The practical implications of the model are that it will provide public and private decision makers with a useful tool for forecasting short-term demand for health-care services, enabling better planning for health-care manpower, facility planning, and financial planning needs. The contribution of this paper contrasts with the earlier work in that it supports short-term operational, not strategic, planning needs. The paper's limitation is that it relies on data from one state. It should be tested in other, dissimilar, areas of the United States.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

The application of forecasting to health care is not new. A frequent issue in many Inpatient Rehabilitation Facilities (IRFs) is the fluctuating and unpredictable census. With scarce resources, particularly physical therapists and occupational therapists, this unpredictability makes appropriate scheduling of these resources challenging. This research addresses the issue of patient admissions in an inpatient rehabilitation facility attached to an 861 bed level-one trauma hospital. The goal is to develop a predictive model for the IRF's Census to assist in resource planning (e.g., labor, beds, and materials).

The authors have previously validated a design of the health-care supply chain which treats patients as inventory without loss of respect for the patients. This work continues examination of patients as inventory while addressing the dual objectives of reducing redundancy in services and creating greater efficiency in the health-care supply chain. Historical data is used to forecast health care needs in light of the increasingly specialized health-care professionals, which have resulted in much more flexible and expensive supply chains. The lack of common data storage, or electronic medical records (EMRs), has created a need for redundancy (or rework) in medical testing. The use of EMR will also enhance our ability to forecast needs in the future. We perform simulations using SigmaFlow software to address our goals relative to the resource constraints, monetary constraints, and the overall culture of the medical supply chain. The simulation outcomes lead us to recommendations for data warehousing as well as providing mechanisms, like inventory postponement strategies, to establish structures for more efficiency, and reduced flexibility in the supply chains.

This qualitative study explores how individuals understand health insurance concepts and make health insurance purchase decisions. The study sought to develop a model of the health insurance decision-making process.

This study used semi-structured interview questions and the micro-moment time-line interview technique with newly hired employees to discuss the steps that individuals follow when making health insurance decisions. The researcher used an open coding approach to analyze the steps listed by each participant, and emergent themes were used to code all interview transcripts in Atlas.ti.

This study identified information tactics used by individuals when evaluating health insurance documentation. The findings also shed light on the personal reflection individuals undertake when making their health insurance choices.

The information needs and preferred information sources identified in this study will be of interest to information professionals and human resources officers providing assistance with health insurance enrolment.

The findings demonstrating that participants characterized their health insurance choice as a shared decision is a novel contribution of this study.

Medical device industry in India is a niche sector with few key players but it possesses huge potential for both domestic and international trade. In recent years, a number of regulatory relaxations have been provided to medical device manufacturers in India to enhance production and further trade especially exports. Though the industry is highly dependent on imports, the purpose of this paper is to identify key medical devices using the revealed comparative advantage, which can be exported from India by identifying new markets.

For the selected medical devices, India’s exports to the world and the newly identified markets are forecasted using the autoregressive integrated moving average model of regression.

It is seen that three major medical devices emerge to be the ones where India has the capacity and potential to manufacture and export. These medical devices are electro-cardiographs, magnetic resonance imaging apparatus and oscilloscopes and oscillographs being exported to the USA, Australia; China and the USA, respectively, which is rising in recent years.

As the forecasted values indicate that there is an increasing potential in exports from India to the world of the selected medical devices, there is an urgent need to develop this industry and enhance exports from India. Very few studies have been carried out to examine and forecast exports from specific sectors or industries which is the need of the hour now.

The paper also provides suggestions to exporters and policymakers on leveraging the future export potential of selected medical devices.

Purpose: This chapter describes the main features of the financing of health care expenditure in the French health care system.

Methodology/Approach: This chapter presents key reforms that have been implemented to make the health care system more sustainable in the main dimensions of care: ambulatory, hospital, pharmaceuticals and insurance coverage.

Findings: Overall, French public authorities have followed three paths to improve the sustainability of the health care system: reducing public expenses, generalising access to complementary health insurance and streamlining care toward the most disadvantaged individuals. Looking in the future, the sustainability of the French health care system will mainly rely on two areas of recommendations. The first area is to respect the national annual target for health insurance spending, with a focus on responsible prescriptions, optimised care pathways and increased use of primary and ambulatory care where possible. The second area is to increase efficiency on the short to medium terms. This includes an increased quality of the care toward patients with a disability or special needs, a clearer engagement of patients within their care pathways to increase treatment compliance, and more generally a search for coordinated care that is fair and appropriate.

It is clear that dramatic changes are taking place in the way health care organizations are being managed. Health care executives are having to make the transition from managing with a “pass the cost along to the consumer” perspective to one of extreme competitive pressure to reduce costs while maintaining appropriate levels of quality. Reports the results of a survey of 135 American hospitals′ executives. Uses factor analysis to uncover four distinct strategies being operationalized. Classifies hospitals′ strategies as: strategic analysers, externally focused and offensively proactive; quality providers, internally‐focused and defensive reactors; price negotiators, externally‐focused and offensively reactive; or cost efficiency providers, internally focused and defensively proactive.

Due to the evolution of the health-care scenario and the growing role of the primary care setting, the distribution processes of health technologies will be more and more in demand in the near future. This paper aims to investigate this theme, analyzing the performance, strengths and weaknesses of the current distribution practices, with the ultimate overarching aim to improve the provision of the primary care services.

The research framework is twofold. First, a tool to monitor the economic/quantitative performance of the distribution models was designed; second, the tool was applied to measure the performance of distribution models of absorbent devices for incontinence adopted by Local Health Authorities (LHA) in Lombardy Region (Italy). Quantitative data were collected by LHAs (from 2012 to 2016) and compared through data-benchmarking. Qualitative data from interviews and focus groups complemented the outcomes.

Two main distribution models were investigated: distribution through pharmacies and home delivery. Results show that there is no winning/preferable model in terms of economic/quantitative performance and service quality level, but a counterbalanced combination of strengths and weaknesses exists. Moving from the highlighted weaknesses and building on the strengths, an alternate distribution model is proposed for testing.

The present study approaches the theme of primary care services with a holistic approach, filling a literature gap. It also provides practitioners with a tool of performance analysis and management and real data, applicable also in international contexts. The collected real-world data also gives insights on the area of the quality of care, with particular reference to the patients’ experience. As a lesson learned, policymakers and the National Healthcare Service should re-think their current distribution models/practices in the light of the highlighted criticisms and opportunities for improvement.

The last 20 years have seen increasing interest in the use of Delphi in a wide range of health‐care applications. However, this use has been accompanied by attempts to codify and define a “true Delphi”. Many authors take a narrow view of the purpose of Delphi and/or advocate a single prescriptive approach to the conduct of a Delphi study. However, as early as 1975, Linstone and Turoff pointed to the danger of attempting to define Delphi as one would immediately encounter a study that violated that definition. Through critical examination of some of the controversies and misunderstandings that surround Delphi, this paper aims to dispel some of the myths and demonstrates the wide scope and potential of this versatile approach.