The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings

Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. The multistakeholder development processes involved syntheses of recommendations from existing guidelines and literature; creation and validation of clinical algorithms; and iterative development, implementation, and evaluation. The common themes surrounding adaptations of IMCI guidance highlight the complexities of digitalizing evidence-based recommendations and reinforce the rationale for leveraging standards for CDSS development, such as the World Health Organization's SMART Guidelines. Implementation through multistakeholder dialogue is critical to ensure CDSSs can effectively and equitably improve quality of care for children in resource-constrained settings.

Polypharmacy and the Change of Self-Rated Health in Community-Dwelling Older Adults

Polypharmacy is associated with poorer self-rated health (SRH). However, whether polypharmacy has an impact on the SRH progression is unknown. This study investigates the association of polypharmacy with SRH change in 1428 participants of the Berlin Initiative Study aged 70 years and older over four years. Polypharmacy was defined as the intake of ≥5 medications. Descriptive statistics of SRH-change categories stratified by polypharmacy status were reported. The association of polypharmacy with being in SRH change categories was assessed using multinomial regression analysis. At baseline, mean age was 79.1 (6.1) years, 54.0% were females, and prevalence of polypharmacy was 47.1%. Participants with polypharmacy were older and had more comorbidities compared to those without polypharmacy. Over four years, five SRH-change categories were identified. After covariate adjustment, individuals with polypharmacy had higher odds of being in the stable moderate category (OR 3.55; 95% CI [2.43-5.20]), stable low category (OR 3.32; 95% CI [1.65-6.70]), decline category (OR 1.87; 95% CI [1.34-2.62]), and improvement category (OR 2.01; [1.33-3.05]) compared to being in the stable high category independent of the number of comorbidities. Reducing polypharmacy could be an impactful strategy to foster favorable SRH progression in old age.

EQRbot: A chatbot delivering EQR argument-based explanations

Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents.

A Specialized Shell for Intelligent Systems of Prescribing Medication

This paper analyzes the existing decision support systems for prescription of drug therapy. The main principles of development and architecture of an intelligent clinical decision support system that is implemented as a specialized shell are described. The unique features of the system, as well as information and software components that are part of it, are shown. The presented examples demonstrate all the proposed solutions.

Transitioning to Digital Systems: The Role of World Health Organization's Digital Adaptation Kits in Operationalizing Recommendations and Interoperability Standards

INTRODUCTION

The transition from paper to digital systems requires quality assurance of the underlying content and application of data standards for interoperability. The World Health Organization (WHO) developed digital adaptation kits (DAKs) as an operational and software-neutral mechanism to translate WHO guidelines into a standardized format that can be more easily incorporated into digital systems.

METHODS

WHO convened health program area and digital leads, reviewed existing approaches for requirements gathering, mapped to established standards, and incorporated research findings to define DAK components.

RESULTS

For each health domain area, the DAKs distill WHO guidelines to specify the health interventions, personas, user scenarios, business process workflows, core data elements mapped to terminology codes, decision-support logic, program indicators, and functional and nonfunctional requirements.

DISCUSSION

DAKs aim to catalyze quality of care and facilitate data use and interoperability as part of WHO's vision of SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines. Efforts will be needed to strengthen a collaborative approach for the uptake of DAKs within the local digital ecosystem and national health policies.

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.

mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review

AIMS

Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support.

METHODS

A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed.

RESULTS

From 18 papers, 11 discrete GDM-based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two-thirds of the apps provided condition-relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum.

CONCLUSIONS

There are limited mHealth apps for GDM that incorporate AI or AI-based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.

A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care

Although clinical decision support systems (CDSSs) are increasingly emphasized as one of the possible levers for improving care, they are still not widely used due to different barriers, such as doubts about systems’ performance, their complexity and poor design, practitioners’ lack of time to use them, poor computer skills, reluctance to use them in front of patients, and deficient integration into existing workflows. While several studies on CDSS exist, there is a need for additional high-quality studies using large samples and examining the differences between outcomes following a decision based on CDSS support and those following decisions without this kind of information. Even less is known about the effectiveness of a CDSS that is delivered during a grand round routine and with the help of socially assistive humanoid robots (SAHRs). In this study, 200 patients will be randomized into a Control Group (i.e. standard care) and an Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR). Health care quality and Quality of Life measures will be compared between the two groups. Additionally, approximately 22 clinicians, who are also active researchers at the University Clinical Center Maribor, will evaluate the acceptability and clinical usability of the system. The results of the proposed study will provide high-quality evidence on the effectiveness of CDSS systems and SAHR in the grand round routine.

Successful implementation of a trans-jurisdictional, primary care, anticipatory care planning intervention for older adults at risk of functional decline: interviews with key health professionals

Background

Aging populations present a challenge to health systems internationally, due to the increasing complexity of care for older adults living with functional decline. This study aimed to elicit expert views of key health professionals on effective and sustainable implementation of a nurse-led, person-centred anticipatory care planning (ACP) intervention for older adults at risk of functional decline in a primary care setting.

Methods

We examined the feasibility of an ACP intervention in a trans-jurisdictional feasibility cluster randomized controlled trial consisting of home visits by research nurses who assessed participants’ health, discussed their health goals and devised an anticipatory care plan following consultation with participants’ GPs and adjunct clinical pharmacist. As part of the project, we elicited the views and recommendations of experienced key health professionals working with the target population who were recruited using a ‘snowballing technique’ in cooperation with older people health networks in the Republic of Ireland (ROI) and Northern Ireland (NI), United Kingdom [n = 16: 7 ROI, 9 NI]. Following receipt of written information about the intervention and the provision of informed consent, the health professionals were interviewed to determine their expert views on the feasibility of the ACP intervention and recommendations for successful implementation. Data were analyzed using thematic analysis.

Results

The ACP intervention was perceived to be beneficial for most older patients with multimorbidity. Effective and sustainable implementation was said to be facilitated by accurate and timely patient selection, GP buy-in, use of existing structures within health systems, multidisciplinary and integrated working, ACP nurse training, as well as patient health literacy. Barriers emerged as significant work already undertaken, increasing workload, lack of time, funding and resources, fragmented services, and geographical inequalities.

Conclusions

The key health professionals perceived the ACP intervention to be highly beneficial to patients, with significant potential to prevent or avoid functional decline and hospital admissions. They suggested that successful implementation of this primary care based, whole-person approach would involve integrated and multi-disciplinary working, GP buy in, patient health education, and ACP nurse training. The findings have potential implications for a full trial, and patient care and health policy.

Trial registration

Clinicaltrials.gov, ID: NCT03902743. Registered on 4 April 2019.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06896-1.

Building a Digital Tool for the Adoption of the World Health Organization's Antenatal Care Recommendations: Methodological Intersection of Evidence, Clinical Logic, and Digital Technology

Background

One of the key mandates of the World Health Organization (WHO) is to develop guidelines, defined as “a document containing recommendations for clinical practice or public health policy.” Guidelines represent the global standard for information sources shaping clinical practice and public health policies. Despite the rigorous development process and the value of guidelines for setting standards, implementing such standards within local contexts and at the point of care is a well-documented challenge. Digital technologies enable agile information management and may facilitate the adaptation of guidelines to diverse settings of health services delivery.

Objective

The objective of this paper is to detail the systematic and iterative process involved in transforming the WHO Antenatal Care (ANC) guidelines into a digital decision-support and patient-record application for routine use in primary health care settings, known as the WHO digital ANC module.

Methods

The WHO convened a team of clinical and digital health experts to develop the WHO digital ANC module as a tool to assist health care professionals in the implementation of WHO evidence-based recommendations for pregnant women. The WHO digital ANC module’s creation included the following steps: defining a minimum viable product (MVP), developing clinical workflows and algorithms, algorithm testing, developing a data dictionary, and the creation of a user interface or application development. The overall process of development took approximately 1 year to reach a stable prototype and to finalize the underlying content requirements of the data dictionary and decision support algorithms.

Results

The first output is a reference software reflecting the generic WHO ANC guideline content, known as the WHO digital ANC module. Within it, all actionable ANC recommendations have related data fields and algorithms to confirm whether the associated task was performed. WHO recommendations that are not carried out by the health care worker are saved as pending tasks on a woman’s health record, and those that are adequately fulfilled trigger messages with positive reinforcement. The second output consists of the structured documentation of the different components which contributed to the development of the WHO digital ANC module, such as the data dictionary and clinical decision support workflows.

Conclusions

This is a novel approach to facilitate the adoption and adaptation of recommendations through digital systems at the health service delivery level. It is expected that the WHO digital ANC module will support the implementation of evidence-based practices and provide information for monitoring and surveillance; however, further evidence is needed to understand how the WHO digital ANC module impacts the implementation of WHO recommendations. Further, the module’s implementation will inform the WHO’s ongoing efforts to create a pathway to adaptive and integrated (Smart) Guidelines in Digital Systems to improve health system quality, coverage, and accountability.

Towards standardisation of evidence-based clinical care process specifications

There is a strong push towards standardisation of treatment approaches, care processes and documentation of clinical practice. However, confusion persists regarding terminology and description of many clinical care process specifications which this research seeks to resolve by developing a taxonomic characterisation of clinical care process specifications. Literature on clinical care process specifications was analysed, creating the starting point for identifying common characteristics and how each is constructed and used in the clinical setting. A taxonomy for clinical care process specifications is presented. The De Bleser approach to limited clinical care process specifications characterisation was extended and each clinical care process specification is successfully characterised in terms of purpose, core elements and relationship to the other clinical care process specification types. A case study on the diagnosis and treatment of Type 2 Diabetes in the United Kingdom was used to evaluate the taxonomy and demonstrate how the characterisation framework applies. Standardising clinical care process specifications ensures that the format and content are consistent with expectations, can be read more quickly and high-quality information can be recorded about the patient. Standardisation also enables computer interpretability, which is important in integrating Learning Health Systems into the modern clinical environment. The approach presented allows terminologies for clinical care process specifications that were widely used interchangeably to be easily distinguished, thus, eliminating the existing confusion.

A framework for automated conflict detection and resolution in medical guidelines

Common chronic conditions are routinely treated following standardised procedures known as clinical guidelines. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.

Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation

BACKGROUND

Medication therapy management (MTM) is a service, most commonly provided by pharmacists, intended to identify and resolve medication therapy problems (MTPs) to enhance patient care. MTM is typically documented by the community pharmacist in an MTM vendor's web-based platform. These platforms often include integrated alerts to assist the pharmacist with assessing MTPs. In order to maximize the usability and usefulness of alerts to the end users (e.g., community pharmacists), MTM alert design should follow principles from human factors science. Therefore, the objectives of this study were to 1) evaluate the extent to which alerts for community pharmacist-delivered MTM align with established human factors principles, and 2) identify areas of opportunity and recommendations to improve MTM alert design.

METHODS

Five categories of MTM alerts submitted by community pharmacists were evaluated: 1) indication, 2) effectiveness; 3) safety; 4) adherence; and 5) cost-containment. This heuristic evaluation was guided by the Instrument for Evaluating Human-Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) which we adapted and contained 32 heuristics. For each MTM alert, four analysts' individual ratings were summed and a mean score on the modified I-MeDeSA computed. For each heuristic, we also computed the percent of analyst ratings indicating alignment with the heuristic. We did this for all alerts evaluated to produce an "overall" summary of analysts' ratings for a given heuristic, and we also computed this separately for each alert category. Our results focus on heuristics where ≤50% of analysts' ratings indicated the alerts aligned with the heuristic.

RESULTS

I-MeDeSA scores across the five alert categories were similar. Heuristics pertaining to visibility and color were generally met. Opportunities for improvement across all MTM alert categories pertained to the principles of alert prioritization; text-based information; alarm philosophy; and corrective actions.

CONCLUSIONS

MTM alerts have several opportunities for improvement related to human factors principles, resulting in MTM alert design recommendations. Enhancements to MTM alert design may increase the effectiveness of MTM delivery by community pharmacists and result in improved patient outcomes.