Clinical decision support: Converging toward an integrated architecture

A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder

OBJECTIVE

Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD.

METHODS

Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs.

RESULTS

Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs (n = 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used (n = 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups.

CONCLUSION

Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.

Health information technology foresight for Iran: A Delphi study of experts' views to inform future policymaking

BACKGROUND

Policymakers require a systematic approach when planning for information technology needs in healthcare.

OBJECTIVE

The aim of this study was to obtain experts' predictions of future health information technology (HIT) needs until 2025 for Iran in relation to the relative importance of key technologies, expected timeframe of realisation, areas that may be impacted upon and obstacles to achieving these goals.

METHOD

This article presents results from the third phase (a Delphi study) of a larger mixed-method study. Policymakers from the Iranian Ministry of Health and faculty members from different medical universities across the country who were expert in the field of HIT were invited to participate (n = 61).

RESULTS

Participants (39) completed the first-round questionnaire and 24 completed the second. The development of personal health records (n = 32, 82.0%), the development of clinical decision-making systems (n = 30, 76.9%) and the use of business intelligence for collecting and analysing clinical and financial data (n = 32, 82.0%) were predicted to occur after 2025. The healthcare areas predicted to experience the greatest impact from most HITs were facilitating patient-provider communication and improving healthcare quality. Key barriers to achieving HITs were related to weaknesses in planning and limited financial resources for most technologies.

CONCLUSION

By identifying the areas of impact and the barriers to achieving the HIT goals, more accurate planning is possible and resources can be allocated according to priorities.

Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy

Purpose

This paper aims to investigate the role of business intelligence systems (BIS) in the relationship between organizational learning culture and organizational performance in the health-care context.

Design/methodology/approach

A quantitative approach was applied to identify the hypothesized relationships. Data were obtained from a survey of 217 mid- and top-level managers of health-care organizations in Bangladesh. Structural equation modeling was used to analyze the data.

Findings

The findings attest to the effectiveness of the mediating role of BIS in the relationship between organizational learning culture and organizational performance within health-care organizations. The theoretical and practical implications of the findings are also discussed.

Research limitations/implications

The study data were collected from a single country, which made generalization difficult. Further research should be carried out to replicate the present study with different organizational cultural variables and organizational outcomes, such as patient satisfaction.

Practical implications

This study provides a clear direction for hospital managers to invest more resources in an effective learning culture, to be positively mediated by BIS and to eventually enhance their hospitals’ performance. In addition, this study also suggests that hospital managers should focus on building a knowledge-based learning culture, to effectively use the information provided by BIS.

Originality/value

Despite the prior study on the applications of BIS and their value to an organization, little is known about the impact of organizational learning culture on BIS in health-care organizations. The findings give support to the argument that organizational learning culture plays an important role in BIS that, in turn, affects business performance.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

BACKGROUND

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.

OBJECTIVE

The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.

METHODS

The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.

RESULTS

Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.

CONCLUSIONS

MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.