scholarly journals Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

ACI Open ◽  
2020 ◽  
Vol 04 (02) ◽  
pp. e108-e113
Author(s):  
Sally L. Baxter ◽  
Jeremy S. Bass ◽  
Amy M. Sitapati
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Implementation Process ◽  
Unintended Consequences ◽  
Risk Scores ◽  
Implementation Barriers ◽  
New Model ◽  
Home Health Services ◽  
Key Stakeholders ◽  
Discharged Patients

Abstract Background Electronic health record (EHR) vendors now offer “off-the-shelf” artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR. Objectives The aim is to conduct a case study centered on identifying barriers to uptake/utilization. Methods A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies. Results We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services). Conclusion AI models for risk stratification, even if “off-the-shelf” by design, are unlikely to be “plug-and-play” in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.

Implementation Factors Related to Improving the Food Environment Within Health Care Institutions: A Multiple Case Study

2021 ◽  
Author(s):  
Elizaveta Walker ◽  
Robin L Baker ◽  
Kerth O'Brien ◽  
Lynne C Messer ◽  
Cara L Eckhardt ◽  
...  
Keyword(s):  
Health Care ◽  
Food Environment ◽  
Implementation Process ◽  
System Level ◽  
Implementation Barriers ◽  
Executive Leaders ◽  
Sugar Sweetened Beverage ◽  
Implementation Challenges ◽  
Health Care Institutions ◽  
Facilitators And Barriers

Abstract Background Healthy food environment policies (HFEPs), such as sugar-sweetened beverage bans or nutritional standards for vending machines, can improve the healthfulness of retail food venues, particularly within health care institutions that have a health-focused mission. The degree to which operational managers’ and executive leaders’ perceptions of implementation challenges align or diverge, and the extent to which these perceptions affect HFEP implementation, is unknown.Methods We conducted ten semi-structured key informant interviews with managers and executive leaders who participated in HFEP development within five health care organizations. Interviews explored facilitators and barriers for HFEP adoption and maintenance. We transcribed, coded, and analyzed interviews and derived contextual facilitators and barriers.Results We identified 27 facilitators and 30 barriers, which were refined into six and five categories, respectively, and ultimately paired to create three overarching recommendations. Operational managers’ and executive leaders’ perceptions overlapped 44-75% when identifying facilitators but only 33-58% when identifying implementation barriers. Interpersonal issues such as over-delegation and mistrust were prominent among those organizations whose respondents’ perceptions diverged substantially.Conclusion As the obesity epidemic continues to increase, understanding key facilitators and barriers to HFEPs, as well as the influence on leaderships’ perceptions on the implementation process, will be key to addressing obesogenic food environments. Though leaders were generally aligned in perspectives regarding facilitators, there was greater divergence when barriers were discussed. Executive leaders are encouraged to familiarize themselves with operational barriers and refrain from over-delegating these challenges to their operational counterparts, who lack the institutional authority to override organizational or system-level decisions.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Some results of the implementation of the priority project «creating a new model of a medical organization that provides primary health care»: problems and prospects

2020 ◽  
pp. 35-43
Author(s):  
Alexey Smyshlyaev ◽  
Maria Sadovskaya
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Quality Management System ◽  
Primary Health Care System ◽  
Medical Organization ◽  
New Model ◽  
System A ◽  
Primary Health ◽  
The Creation ◽  
Care Problems

Optimization of the activities of medical organizations providing primary health care requires the development of new organizational and functional models. The introduction of new approaches to organizing the activities of medical organizations is primarily a step towards patients. The new model is a patient-oriented medical organization, the management of which is based on the use of a process-oriented approach and «lean» technologies. Since 2019, within the framework of the federal project «Development of a primary health care system,» a project has been launched to introduce the «New Model of a Medical Organization Providing Primary Health Care». The implementation of the project is scheduled for 2019-2024 inclusive. The creation and replication of the «new model» is planned for the participation of all subjects of the Russian Federation. The introduction of lean technology methods in the work of medical organizations has reduced the waiting time for doctors, optimized the burden on doctors, reduced the time for obtaining research results, streamlining the process of moving a patient within a medical organization. The creation of an effective quality management system in medical organizations is achieved through the phased implementation of lean-technology.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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