Exploring business models for managing uncertainty in healthcare, medical devices, and biotechnology industries.

The medical devices, biotechnology, and healthcare industries are closely tied to innovation, with companies relying on research and development and new product introduction. However, innovation activities like research and development can be costly, and commercializing new products faces complexities. Having effective business models (BMs) aligned with such innovation activities is crucial. This study delves into the intricacies of BMs in these innovator companies, emphasizing the pivotal role of effective BMs in navigating uncertainties and fostering innovation. This study conducted a systematic literature review to synthesize knowledge on BMs in innovator health-tech companies and compare models on dimensions like infrastructure, offering, customers, and finances. The review of 34 recent papers revealed 9 key BMs - open innovation, sustainable, dynamic, dual, spin-off, frugal, high-tech entrepreneurial content marketing, back-end, and product-service systems BMs. The analysis found open innovation, sustainability, and dynamicity as foundational models that can serve as a basis when combined with others. The paper unveils a tailored Dynamic Sustainable Business Model (DSBM) for Health-Tech, designed to integrate adaptability and sustainability, providing a framework for companies to leverage emerging technologies effectively. Additionally, a conceptual framework outlining 28 groups of uncertainty factors in BMs was developed to aid risk management in health-tech. The findings offer crucial insights for companies in health-tech industries, aiding them in managing innovation and value creation amidst a rapidly evolving landscape.

Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis.

OBJECTIVES: The prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors. STUDY DESIGN: The design of this study is a combination of the prediction method and empirical analysis. METHODS: By organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis. RESULTS: Owing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self-memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/105). CONCLUSIONS: The grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems.

Day surgery appointment scheduling with patient preferences and stochastic operation duration.

BACKGROUND: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. OBJECTIVE: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. METHODS: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. RESULTS: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. CONCLUSIONS: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

On unified framework for discrete-time grey models: Extensions and applications.

Grey theory-based time series models are widely used in various fields and disciplines. While most of the research is focused on the development and improvement of novel discrete-time models, very limited attention has been paid to the relationships among diverse models. The current paper proposes a methodological and practical framework to unify the single-variable, multi-variable, and multi-output discrete-time grey models, making it easier for practitioners to select an appropriate model for a given time-series forecasting problem. The recursive extrapolation strategy is present to generate multi-step ahead forecasts and four model families are deduced within the universal framework. Large-scale simulation studies are conducted to evaluate the finite-sample fitting and multi-step ahead forecasting performance. The proposed approach is illustrated using application examples from the indirect measurement of the tensile strength of materials.