Enhancement of decision rules to increase generalizability and performance of the rule-based system assessing risk for pressure ulcer

Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review

BACKGROUND

Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients.

OBJECTIVE

This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis.

METHODS

A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included.

RESULTS

The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development.

CONCLUSIONS

There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

Replication studies in the clinical decision support literature-frequency, fidelity, and impact

OBJECTIVE

To assess the frequency, fidelity, and impact of replication studies in the clinical decision support system (CDSS) literature.

MATERIALS AND METHODS

A PRISMA-compliant review identified CDSS replications across 28 health and biomedical informatics journals. Included articles were assessed for fidelity to the original study using 5 categories: Identical, Substitutable, In-class, Augmented, and Out-of-class; and 7 IMPISCO domains: Investigators (I), Method (M), Population (P), Intervention (I), Setting (S), Comparator (C), and Outcome (O). A fidelity score and heat map were generated using the ratings.

RESULTS

From 4063 publications matching search criteria for CDSS research, only 12/4063 (0.3%) were ultimately identified as replications. Six articles replicated but could not reproduce the results of the Han et al (2005) CPOE study showing mortality increase and, over time, changed from truth testing to generalizing this result. Other replications successfully tested variants of CDSS technology (2/12) or validated measurement instruments (4/12).

DISCUSSION

A replication rate of 3 in a thousand studies is low even by the low rates in other disciplines. Several new reporting methods were developed for this study, including the IMPISCO framework, fidelity scores, and fidelity heat maps. A reporting structure for clearly identifying replication research is also proposed.

CONCLUSION

There is an urgent need to better characterize which core CDSS principles require replication, identify past replication data, and conduct missing replication studies. Attention to replication should improve the efficiency and effectiveness of CDSS research and avoiding potentially harmful trial and error technology deployment.

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

BACKGROUND

The clinical decision-making process in pressure ulcer management is complex, and its quality depends on both the nurse's experience and the availability of scientific knowledge. This process should follow evidence-based practices incorporating health information technologies to assist health care professionals, such as the use of clinical decision support systems. These systems, in addition to increasing the quality of care provided, can reduce errors and costs in health care. However, the widespread use of clinical decision support systems still has limited evidence, indicating the need to identify and evaluate its effects on nursing clinical practice.

OBJECTIVE

The goal of the review was to identify the effects of nurses using clinical decision support systems on clinical decision making for pressure ulcer management.

METHODS

The systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. The search was conducted in April 2019 on 5 electronic databases: MEDLINE, SCOPUS, Web of Science, Cochrane, and CINAHL, without publication date or study design restrictions. Articles that addressed the use of computerized clinical decision support systems in pressure ulcer care applied in clinical practice were included. The reference lists of eligible articles were searched manually. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies.

RESULTS

The search strategy resulted in 998 articles, 16 of which were included. The year of publication ranged from 1995 to 2017, with 45% of studies conducted in the United States. Most addressed the use of clinical decision support systems by nurses in pressure ulcers prevention in inpatient units. All studies described knowledge-based systems that assessed the effects on clinical decision making, clinical effects secondary to clinical decision support system use, or factors that influenced the use or intention to use clinical decision support systems by health professionals and the success of their implementation in nursing practice.

CONCLUSIONS

The evidence in the available literature about the effects of clinical decision support systems (used by nurses) on decision making for pressure ulcer prevention and treatment is still insufficient. No significant effects were found on nurses' knowledge following the integration of clinical decision support systems into the workflow, with assessments made for a brief period of up to 6 months. Clinical effects, such as outcomes in the incidence and prevalence of pressure ulcers, remain limited in the studies, and most found clinically but nonstatistically significant results in decreasing pressure ulcers. It is necessary to carry out studies that prioritize better adoption and interaction of nurses with clinical decision support systems, as well as studies with a representative sample of health care professionals, randomized study designs, and application of assessment instruments appropriate to the professional and institutional profile. In addition, long-term follow-up is necessary to assess the effects of clinical decision support systems that can demonstrate a more real, measurable, and significant effect on clinical decision making.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42019127663; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=127663.

Patients Decision Aid System Based on FHIR Profiles

Patients are becoming more and more involved in clinical decision-making process. Several factors support this process. Advances in omics allows individualization of diagnosis and treatment. Patient awareness and easy availability of data on the Internet allows patients to become informed decision makers when it comes even to disease management. Mass media emphasize the issue of medical errors, making patients demanding for quality in medical care. In some healthcare settings, patents face a problem of interpreting medical data and making decisions on treatment tactics without having a doctor, who could potentially support them. Delegating this task to a Patient Decision Aide system can add automatically generated recommendations to result reports without adding significant workload on the doctors, increase patients' motivation and support their decisions. We have implemented a patient decision aid system based on the productions rules, which: Collects data from available sources; Automatically analyses and interprets laboratory test results; Recommends running additional tests for a more precise diagnostic; Delivers automatically generated reports to doctors and patients in a natural language. To achieve semantic interoperability with other systems we have implemented a FHIR engine. The knowledge base has been organized as a graph structure. The application is structured as a set of lightly coupled services, which implement the logic of the decision support system. In total, we have modelled 365 nodes of test components, 5084 nodes of inference rules, 49932 connections and 3072 blocks of text for medical certificates. The findings of the research provide a deep understanding of how the semantically interoperable clinical decision support systems are implemented. Advances in notification the patients with the elements of patient decision aid is important for clinical data management, and for patients' empowerment and protection. We suppose that the system empowering patients in such way can play a meaningful role in helping patients to make informed decisions during the process of diagnostics and treatment.

Personalization and Patient Involvement in Decision Support Systems: Current Trends

OBJECTIVES

This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care.

METHODS

We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support.

RESULTS

We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness.

CONCLUSIONS

Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

From bench to bed: bridging from informatics theory to practice. An exploratory analysis

BACKGROUND

In 2009, the journal Applied Clinical Informatics (ACI) commenced publication. Focused on applications in clinical informatics, ACI was intended to be a companion journal to METHODS of Information in Medicine (MIM). Both journals are official journals of IMIA, the International Medical Informatics Association.

OBJECTIVES

To explore, after five years, which congruencies and interdependencies exist in publications of these journals and to determine if gaps exist. To achieve this goal, major topics discussed in ACI and in MIM had to be analysed. Finally, we wanted to explore, whether the intention of publishing these companion journals to provide an information bridge from informatics theory to informatics practice and from practice to theory could be supported by this model. In this manuscript we will report on congruencies and interdependencies from practise to theory and on major topis in ACI. Further results will be reported in a second paper.

METHODS

Retrospective, prolective observational study on recent publications of ACI and MIM. All publications of the years 2012 and 2013 from these journals were indexed and analysed.

RESULTS

Hundred and ninety-six publications have been analysed (87 ACI, 109 MIM). In ACI publications addressed care coordination, shared decision support, and provider communication in its importance for complex patient care and safety and quality. Other major themes included improving clinical documentation quality and efficiency, effectiveness of clinical decision support and alerts, implementation of health information technology systems including discussion of failures and succeses. An emerging topic in the years analyzed was a focus on health information technology to predict and prevent hospital admissions and managing population health including the application of mobile health technology. Congruencies between journals could be found in themes, but with different focus in its contents. Interdependencies from practise to theory found in these publications, were only limited.

CONCLUSIONS

Bridging from informatics theory to practise and vice versa remains a major component of successful research and practise as well as a major challenge.