Implementation of Digital Health Ethics: A First Step with the Adoption of 16 European Ethical Principles for Digital Health.

The potential for technology to transform health care is tremendous, but advances in digital health may also bring privacy and data security challenges that may exacerbate inequalities. Hence, it is critical that the development of digital health is included in a framework of humanistic and ethical values. France drew up its roadmap for accelerating the shift towards digital health with ethics at the forefront, along with security and interoperability pillars. Criteria such as digital health for all, transparency of data processing, trustworthy AI, and eco-responsibility and sustainability of digital health were elaborated. Under the French Presidency of the Council of the European Union, building on the proposal of ethical criteria from France, eHealth network representatives unanimously adopted 16 European ethical principles for digital health, formalizing trust commitments towards European citizens and paving the way for the European Health Data Space.

Measured Performance and Healthcare Professional Perception of Large Language Models Used as Clinical Decision Support Systems: A Scoping Review.

The healthcare sector confronts challenges from overloaded tumor board meetings, reduced discussion durations, and care quality concerns, necessitating innovative solutions. Integrating Clinical Decision Support Systems (CDSSs) has a potential in supporting clinicians to reduce the cancer burden, but CDSSs remain poorly used in clinical practice. The emergence of OpenAI's ChatGPT in 2022 has prompted the evaluation of Large Language Models (LLMs) as potential CDSSs for diagnosis and therapeutic management. We conducted a scoping review to evaluate the utility of LLMs like ChatGPT as CDSSs in several medical specialties, particularly in oncology, and compared users' perception of LLMs with the actually measured performance of these systems.

Reducing the Environmental Impact of Digital Health: Development of an Ecoscore for Health Apps.

The digital revolution is perceived as minimizing the environmental impact of human activities. However, digital actually uses real physical equipment, that consumes natural resources and emits greenhouse gases. The French Ministry of health has very soon engaged in the development of an ethical digital health with a strong concern for eco-responsibility. We have developed an Ecoscore service to allow providers of web and mobile health apps to calculate the environmental impact of their apps. The ecoscore is computed on a script describing the canonical use of the app. It is based on parameters actually measured, data flow (data downloaded from datacenters), performance (time taken to display the result of an action), and energy consumption (battery discharge rate). The French Ministry of Health has made the ecoscore mandatory to software providers candidating to the service catalog of Mon espace santé, a digital health space offered to all French citizens.

Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.

Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.

Leveraging Rule-Based NLP to Translate Textual Reports as Structured Inputs Automatically Processed by a Clinical Decision Support System.

Using clinical decision support systems (CDSSs) for breast cancer management necessitates to extract relevant patient data from textual reports which is a complex task although efficiently achieved by machine learning but black box methods. We proposed a rule-based natural language processing (NLP) method to automate the translation of breast cancer patient summaries into structured patient profiles suitable for input into the guideline-based CDSS of the DESIREE project. Our method encompasses named entity recognition (NER), relation extraction and structured data extraction to systematically organize patient data. The method demonstrated strong alignment with treatment recommendations generated for manually created patient profiles (gold standard) with only 2% of differences. Moreover, the NER pipeline achieved an average F1-score of 0.9 across the main entities (patient, side, and tumor), of 0,87 for relation extraction, and 0.75 for contextual information, showing promising results for rule-based NLP.

A comparison of English and French approaches to providing patients access to Summary Care Records: scope, consent, cost.

Online access to records is part of the process of empowering patients. National health services in both France and England have introduced systems to provide online access to summary health data. The English system was called the "Summary Care Record (SCR)," made accessible to patients through "HealthSpace". The French system Dossier Médical Personnel (DMP) is a patient controlled record clinicians enter data into. The objective was to compare the programmes and lessons from the introduction of patient access. We carried out a literature review. The English system has been progressively de-scoped, with HealthSpace due to close in 2013, only 0.01% of the population signing up for "advanced accounts". The French system slowly grows as more documents are added; though only 0.31% of the population have opened a DMP. The English SCR has an opt-out consent model, whereas the French DMP is patient controlled opt-in consent model. The SCR sits within an NHS intranet while the DMP sits on the Internet. Both systems have costs of around 200 million Euro. Providing patients online access to their medical records is potentially empowering. However, the English HealthSpace and SCR have failed to deliver and are due to be withdrawn as methods of providing patients online access. The French system is still in operation but much criticized for its high costs and low uptake. The design of these systems does not appear to have met patients' needs or been readily integrated into physicians workflow.

Simultaneously authoring and modeling clinical practice guidelines: a case study in the therapeutic management of type 2 diabetes in France.

By providing patient-specific advice, clinical decision support systems (CDSSs) are expected to promote the implementation of clinical practice guidelines (CPGs) to improve the quality of care. However, produced as texts, often incomplete and ambiguous, CPGs are difficult to translate into the formal knowledge bases (KBs) of CDSSs. The French National Authority for Health (HAS) decided to update CPGs on the management of type 2 diabetes. This work illustrates the simultaneous development of the text and its formal counterpart in a CDSS named RecosDiab. CPGs were elaborated by a working group according to the guideline development methodology. Textual recommendations were graded, either as evidence-based when evidence existed or as consensus-based when acknowledge by the working group. Knowledge modeling was performed following the steps of de-abstraction, disambiguation, and verification of completeness. This last step generated clinical situations not explicitly mentioned in the text and were graded as expert-based. The resulting KB provides therapeutic advice for 805 clinical situations, among which 2 are graded as evidence-based, 37 are consensus-based, and 766 are expert-based. However, because of the amount of expert-based propositions, the HAS did not endorse the system.

Clinical Decision Support Systems Applied to the Management of Breast Cancer Patients: A Scoping Review.

Breast cancer is the most commonly diagnosed cancer worldwide, and its burden has been rising over the past decades. A significant advance in healthcare is the integration of Clinical Decision Support Systems (CDSSs) into medical practice, which support healthcare professionals improving clinical decisions, leading to recommended patient-specific treatments and enhanced patient care. Breast cancer CDSSs are thus currently expanding, whether applied to screening, diagnostic, therapeutic or follow-up tasks. We conducted a scoping review to study their availability and use in practice. Except risk calculators, very few CDSSs are currently routinely used.

Developing a Comprehensive Search Strategy for the Systematic Review of Clinical Decision Support Systems for Nursing Practice.

The search strategy of a literature review is of utmost importance as it impacts the validity of its findings. In order to build the best query to guide the literature search on clinical decision support systems applied to nursing clinical practice, we developed an iterative process capitalizing on previous systematic reviews published on similar topics. Three reviews were analyzed relatively to their detection performance. Errors in the choice of keywords and terms used in title and abstract (missing MeSH terms, failure to use common terms), may make relevant articles invisible.

Elicitating patient patterns of physician non-compliance with breast cancer guidelines using formal concept analysis.

Because they provide patient-specific guideline-based recommendations, clinical decision support systems (CDSSs) are expected to promote the implementation of clinical practice guidelines (CPGs). OncoDoc2 is a CDSS applied to the management of breast cancer. However, despite it was routinely used during weekly multidisciplinary staff meetings (MSMs) at the Tenon Hospital (Paris, France), the compliance rate of MSMs' decisions with CPGs did not reach 100%. Formal Concept Analysis (FCA) has been applied to elicit formal concepts related to non-compliance. A statistical pre-treatment of attributes has been proposed to leverage FCA and discriminate between compliant and non-compliant decisions. Among the 1,889 decisions made over a 3 year-period, 199 decisions of recommended re-excisions have been considered for analysis. In this sample, non-compliance was explained by uncommon clinical profiles and specific patient-centred clinical criteria.

Accuracy of clinical data entry when using a computerized decision support system: a case study with OncoDoc2.

Some studies suggest that the implementation of health information technology (HIT) introduces unpredicted and unintended consequences including e-iatrogenesis. OncoDoc2 is a guideline-based clinical decision support system (CDSS) applied to the management of breast cancer. The system is used by answering closed-ended questions in order to document patient data while navigating through the knowledge base until the best patient-specific recommended treatments are obtained. OncoDoc2 has been used by three hospitals in real clinical settings and for genuine patients. We analysed 394 navigations, recorded on a 10-month period, which correspond to 6,025 data entries. The data entry error rate is 4.2%, spread over 52% of incorrect navigations (N-). However, the overall compliance rate of clinical decisions with guidelines significantly increased from 72.8% (without CDSS) to 87.3% (with CDSS). Although this increase is lowered because of N- navigations (compliance rates are respectively 95% and 80% for N+ and N- navigations), the benefits of HIT outweighted its disadvantages in our study.

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases.

In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.

Are People Ready to Report Digital Health Ethical Issues in Order to Contribute to Their Resolution?

Although guaranteed by the GDPR, transparency of health data processing may not be fully respected, leading citizens to mistrust eHealth and discard digital health services. Identifying and safeguarding ethics in eHealth services is thus important to promote their development. We conducted a survey to assess the extent of ethical issues induced by the use of digital health services, understand the efforts citizens would be willing to accept for reporting such issues, and evaluate citizens' expectations regarding this reporting. Among 200 respondents, 36% reported having encountered ethical issues with the processing of their health data or with digital health services being poorly inclusive. Faced to ethical issues when using a digital health service, 49% of respondents were rather or very angry, and 33% felt rather or very dependent. Most respondents were ready to report digital health ethical issues if there is a feedback for each report.

Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries.

Most clinical texts including breast cancer patient summaries (BCPSs) are elaborated as narrative documents difficult to process by decision support systems. Annotators have been developed to extract the relevant content of such documents, e.g., MetaMap and cTAKES, that work with the English language and perform concept mapping using UMLS, SIFR and ECMT, that work for the French language and provide concepts using various terminologies. We compared the four annotators on a sample of 25 French BCPSs, pre-processed to manage acronyms and translated in English. We observed that MetaMap extracted the largest number of UMLS concepts (15,458), followed by SIFR (3,784), ECMT (1,962), and cTAKES (1,769). Each annotator extracted specific valuable information, not proposed by the other annotators. Considered as complementary, all annotators should be used in sequence to optimize the results.

Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review.

Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method. We performed a scoping review to evaluate the methods used for comparing two CPGs. We searched for methods for extracting CPG components and for methods used for comparing CPGs at different levels of abstraction. In each case, computerized and semi-computerized methods were recognized. Expert knowledge has yet a determinant role for assessing the comparisons, this role being more prominent for the extraction of semantic rules and the resolution of inconsistencies.

Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients.

Complex breast cancer cases that need further multidisciplinary tumor board (MTB) discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc, through the implementation of machine learning procedures and algorithms (Decision Trees, Random Forests, and XGBoost). F1-score after cross-validation, sampling implementation, with or without feature selection, did not exceed 40%.

ABiMed: Towards an Innovative Clinical Decision Support System for Medication Reviews and Polypharmacy Management.

Polypharmacy in elderly is a public health problem with both clinical (increase of adverse drug events) and economic issues. One solution is medication review, a structured assessment of patients' drug orders by the pharmacist for optimizing the therapy. However, this task is tedious, cognitively complex and error-prone, and only a few clinical decision support systems have been proposed for supporting it. Existing systems are either rule-based systems implementing guidelines, or documentary systems presenting drug knowledge. In this paper, we present the ABiMed research project, and, through literature reviews and brainstorming, we identified five candidate innovations for a decision support system for medication review: patient data transfer from GP to pharmacists, use of semantic technologies, association of rule-based and documentary approaches, use of machine learning, and a two-way discussion between pharmacist and GP after the medication review.

Computing the compliance of physician drug orders with guidelines using an OWL2 reasoner and standard drug resources.

Assessing the conformity of a physician's prescription to a given recommended prescription is not obvious since both prescriptions are expressed at different levels of abstraction and may concern only a subpart of the whole order. Recent formalisms (OWL2) and tools (reasoners) from the semantic web technologies are becoming available to represent defined concepts and to handle classification services. We propose a generic framework based on such technologies, using available standardized drug resources, to compute the compliance of a given drug order to a recommended prescription, such that the subsumption relationship yields the conformity relationship between the order and the recommendation. The ATC drug classification has been used as a local ontology. The method has been successfully implemented for arterial hypertension management for which we had a sample of antihypertensive orders. However, supplemental standardized drug knowledge is needed to correctly compare drug orders to recommended orders.

A generic system for critiquing physicians' prescriptions: usability, satisfaction and lessons learnt.

Clinical decision support systems have been developed to help physicians to take clinical guidelines into account during consultations. The ASTI critiquing module is one such systems; it provides the physician with automatic criticisms when a drug prescription does not follow the guidelines. It was initially developed for hypertension and type 2 diabetes, but is designed to be generic enough for application to all chronic diseases. We present here the results of usability and satisfaction evaluations for the ASTI critiquing module, obtained with GPs for a newly implemented guideline concerning dyslipaemia, and we discuss the lessons learnt and the difficulties encountered when building a generic DSS for critiquing physicians' prescriptions.