Healthcare decision support system for administration of chronic diseases

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

BACKGROUND

The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.

METHOD

This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.

RESULTS

Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.

CONCLUSION

AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus

BACKGROUND

Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time.

OBJECTIVE

In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients.

METHODS

Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated.

RESULTS

After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients.

CONCLUSION

The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

A rule-based decision support system for aiding iron deficiency management

Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health problems including "anemia." In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency-ANEmia- Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients' demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.

A Fuzzy-Based Customisation of Healthcare Knowledge to Support Clinical Domestic Decisions for Chronically Ill Patients

The development of customised healthcare systems is becoming an important issue in the healthcare industry due to the rapid increase in the number of chronically ill patients. These systems aim to deliver effective care to patients having chronic diseases through customised services. However, knowledge bases need also to be customised since systems are confronted with huge amount of personalised and imprecise medical knowledge. Therefore, we propose in this paper a new system to customise medical knowledge according to progressive disease phases and pathological cases. A rule management process first customises rules according to the specificities of every disease phase, and then matches a private knowledge base with each enrolled patient. This base contains only the patient’s customised knowledge. After reasoning, another customisation process is carried out by the component, Result Manager, which ensures the validation of the system outcomes by the pathological case experts, before being recommended. This will better ensure the recommendation of the generated results to the non-professional users. In addition, Result Manager offers fuzzy semantic queries to the experts. In conclusion, our new decision support system makes medical aid decisions not only addressed to physicians, but also to chronically ill patients and persons regarded as caregivers.

Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data

Background

Personal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.

Objective

By analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.

Methods

We gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.

Results

The total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant.

Conclusions

Although a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.