Patient facing decision support system for interpretation of laboratory test results

Improving providers' survival estimates and selection of prognosis- and guidelines-appropriate treatment for patients with symptomatic bone metastases: Development of the Bone Metastases Ensemble Trees for Survival Decision Support Platform

RATIONALE, AIMS AND OBJECTIVES

In the management of symptomatic bone metastases, current practice guidelines do not provide clear methodology for selecting palliative radiotherapy (RT) regimens based on specific patient and disease features. Decision support aids may offer an effective means for translating the complex data needed to render individualised treatment decisions, yet no such tools are available for use in this setting. Thus, we describe the development of the Bone Metastases Ensemble Trees for Survival-Decision Support Platform (BMETS-DSP), which aims to optimise selection of evidence-based, individualised palliative RT regimens.

METHOD

The Ottawa Decision Support Framework was used as the theoretical basis for development of BMETS-DSP. First, we utilised stakeholder input and review of the literature to assess determinants underlying the provider decision. Based on this assessment and iterative stakeholder feedback, we developed the web-based, provider-facing BMETS-DSP. Consistent with the underlying theoretical framework, our design also included assessment of decision quality using the International Patient Decision Aids Standards (IPDAS) certification checklist.

RESULTS

Stakeholder input and review of 54 evidence-based publications identified the following determinants of the provider decision: estimated prognosis, characteristics of the target symptomatic lesion and the primary cancer type, consideration of alternative interventions, access to patient-specific recommendations, and patient preferences. Based on these determinants, we developed the BMETS-DSP that (1) collects patient-specific data, (2) displays an individualised predicted survival curve, and (3) provides case-specific, evidence-based recommendations regarding RT, open surgery, systemic therapy, and hospice referral to aid in the decision-making process. The finalised tool met IPDAS quality requirements. Preliminary results of a pilot assessment suggest impact of clinical outcomes.

CONCLUSIONS

We describe the successful development of a provider-facing decision support platform to aid in the provision of palliative RT in better alignment with patient and disease features. Impact of the BMETS-DSP on decision outcomes will be further assessed in a randomised, controlled study.

A systematic review of technologies and standards used in the development of rule-based clinical decision support systems

A Clinical Decision Support System (CDSS) is a technology platform that uses medical knowledge with clinical data to provide customised advice for an individual patient's care. CDSSs use rules to encapsulate expert knowledge and rules engines to infer logic by evaluating rules according to a patient's specific information and related medical facts. However, CDSSs are by nature complex with a plethora of different technologies, standards and methods used to implement them and it can be difficult for practitioners to determine an appropriate solution for a specific scenario. This study's main goal is to provide a better understanding of different technical aspects of a CDSS, identify gaps in CDSS development and ultimately provide some guidelines to assist their translation into practice. We focus on issues related to knowledge representation including use of clinical ontologies, interoperability with EHRs, technology standards, CDSS architecture and mobile/cloud access.

This study performs a systematic literature review of rule-based CDSSs that discuss the underlying technologies used and have evaluated clinical outcomes. From a search that yielded an initial set of 1731 papers, only 15 included an evaluation of clinical outcomes. This study has found that a large majority of papers did not include any form of evaluation and, for many that did include an evaluation, the methodology was not sufficiently rigorous to provide statistically significant results. From the 15 papers shortlisted, there were no RCT or quasi-experimental studies, only 6 used ontologies to represent domain knowledge, only 2 integrated with an EHR system, only 5 supported mobile use and only 3 used recognised healthcare technology standards (and all these were HL7 standards). Based on these findings, the paper provides some recommendations for future CDSS development.

Study of patients' attitude to automatic interpretation of laboratory test results and its influence on follow-up rate

Background

One of the current major factors of not following up on the abnormal test results is the lack of information about the test results and missing interpretations. Clinical decision support systems (CDSS) can become a solution to this problem. However, little is known how patients react to the automatically generated interpretations of the test results, and how this can affect a decision to follow up. In this research, we study how patients perceive the interpretations of the laboratory tests automatically generated by a clinical decision support system depending on how they receive these recommendations and how this affects the follow-up rate.

Methods

A study of 3200 patients was done querying the regional patient registry. The patients were divided into 4 groups who received:

Recommendations automatically generated by a CDSS with a clear indication of their automatic nature.

Recommendations received personally from a doctor with a clear indication of their automatic nature.

Recommendations from a doctor with no indication of their automated generation.

No recommendations, only the test results.

A follow-up rate was calculated as the proportion of patients referred to a laboratory service for a follow-up investigation after receiving a recommendation within two weeks after the first test with abnormal test results had been completed and the interpretation was delivered to the patient. The second phase of the study was a research of the patients’ motivation. It was performed with a group of 789 patients.

Results

All the patients who received interpretations on the abnormal test results demonstrated a significantly higher rate of follow-up (71%) in comparison to the patients who received only test results without interpretations (49%). Patients mention a time factor as a significant benefit of the automatically generated interpretations in comparison to the interpretations they can receive from a doctor.

Conclusion

The results of the study show that delivering automatically generated interpretations of test results can support patients in making a decision to follow up. They are trusted by patients and raise their motivations and engagement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01805-w.

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

Training Aspects of Laboratory-Based Decision Support

Pathology has a large role to play in the proper development, implementation, and optimization of clinical decision support (CDS). CDS training must be supported by an educational foundation in clinical and pathology informatics. Educational opportunities are currently limited, but expanding, in the pathology residency space with Pathology Informatics Essentials for Residents. The use of an educational version of electronic clinical systems is an important educational tool to support the needed outcomes-driven and exercise-based informatics and CDS training. With the multidisciplinary nature of informatics, it is advantageous to include laboratory professionals in the training exercises as appropriate.