Management of Open Innovation in Healthcare for Cost Accounting Using EHR

Calandra

BMC Med Inform Decis Mak

et al. 2021

Self Cite

470

127

Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Section: Discussionmentioning

confidence: 99%

The role of artificial intelligence in healthcare: a structured literature review

Calandra

BMC Med Inform Decis Mak

et al. 2021

Self Cite

470

127

“…We determined that greater efficiency can be achieved in the development of health policies and budget distribution at the national level and the training and deployment efficiency of emergency medical personnel in hospitals with the open innovation perspective if a clear number of patients could be predicted for the actual atmospheric environment [44,45]. The preceding studies confirmed that the impact of government policies can be interpreted through the VAR model used for multivariate time series data analysis [46,47], and in other studies there exist the cases which applied DNN models to predict the effectiveness of public health polices [48].…”

Section: Prediction Of the Asthmatic Occurrencementioning

confidence: 99%

Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

et al. 2020

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.

“…The effects of hospital organizational characteristics on performance can differ across public, nonprofit, and profit hospitals. The methods of which hospitals can effectively improve health equity may vary across diverse sectors [37,38]. Competing solutions to a trade-off between efficiency and equity can also vary across government, nonprofit, and for-profit health organizations.…”

Section: Resultsmentioning

confidence: 99%

The Equity of Health Care Spending in South Korea: Testing the Impact of Publicness

Youngju¹,

Kim

Jung

2020

IJERPH

This paper examined the important organizational and managerial factors of publicness for the equity of health care. The extent of organizational publicness was measured with key independent variables such as ownership, evaluation, and accreditation. The dependent variable was measured by three equity indicators for patients under medical care and veterans care: financial inequity, social equity, and overall equity. We analyzed unbalanced panel data with 328 general hospitals between 2008 and 2012. We performed panel analysis with fixed and random effects. Our findings illustrate that government ownership is significantly associated with differences in equity indicators. Government owned hospitals show the better performance for equity than nonprofit and individually owned hospitals do. Compared to nonprofit and individually owned hospitals, government owned hospitals have a higher share of medical payment bills and health care spending for the disadvantaged but a lower proportion of out-of-pocket payment. Government evaluation is also significantly related to better equity performance. There are, however, significantly negative interactions between hospital government ownership and the size of medical payment bills. We found a significant tendency that the more medical payments, the less responsiveness to the equity of health care in government owned hospitals. Future research in hospital performance is required to consider not only sectoral differences but also the negative proclivity of public hospitals that shrink health care services for the poor. Further research is also expected to explore what sectoral identities and behaviors across public, nonprofit, and private hospitals influence the level of equity or inequity in health care.