Personalization and Patient Involvement in Decision Support Systems: Current Trends

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Physicians' strategies for using family history data: having the data is not the same as using the data

Objective

To identify needs in a clinical decision support tool development by exploring how primary care providers currently collect and use family health history (FHH).

Design

Survey questionnaires and semi-structured interviews were administered to a mix of primary and specialty care clinicians within the University of Utah Health system (40 surveys, 12 interviews).

Results

Three key themes emerged regarding providers' collection and use of FHH: (1) Strategies for collecting FHH vary by level of effort; (2) Documentation practices extend beyond the electronic health record's dedicated FHH module; and (3) Providers desire feedback from genetic services consultation and are uncertain how to refer patients to genetic services.

Conclusion

Study findings highlight the varying degrees of engagement that providers have with collecting FHH. Improving the integration of FHH into workflow, and providing decision support, as well as links and tools to help providers better utilize genetic counseling may improve patient care.

Moving Beyond the Rhetoric of Shared Decision-Making: Designing Personal Health Record Technology With Young Adults With Type 1 Diabetes

OBJECTIVES

Engaging young adults with type 1 diabetes (T1D) in the self-management of daily tasks and decision-making provides opportunities for positive health outcomes. However, emerging adulthood and care transitions are associated with decreased clinic attendance and diabetes complications. Shared decision-making (SDM) is an optimal approach for health decisions; however, it has been difficult to implement in practice. Personal health record (PHR) technology is a promising approach for overcoming such barriers. Still, today, PHRs have yet to root themselves into care and present an opportunity for improvement in SDM and engagement in self-management decision-making. The objective of this study was to confirm a functional model of an integrated shared decision-making-personal health record system (e-PHR) by young adults with T1D and care providers.

METHODS

User-centred design approach whereby young adults with T1D, 18 to 24 years of age, and care providers matched PHR functions for the SDM process to confirm an e-PHR functional model.

RESULTS

An e-PHR functional model justified by young adults (n=7) and providers (n=15) was confirmed. The conceptual design was architected within an interconnected digital health ecosystem and integrated 23 PHR functionalities for SDM with a moderate level of agreement between patients and providers (Cohen kappa 0.60 to 0.74).

CONCLUSIONS

The establishment of an e-PHR functional model is a precursor to system design requirements. Results highlight the conceivable value of integrating SDM into PHRs for engagement of young adults with T1D in self-management decision-making. Design implications highlight key challenges for future research and system development, including information exchange across disparate systems, usability considerations and system intelligence for information personalization and decision-support tools.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

OBJECTIVES

This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

METHODS

We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.

RESULTS

We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.

CONCLUSIONS

Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

Shared Decision-Making Ontology for a Healthcare Team Executing a Workflow, an Instantiation for Metastatic Spinal Cord Compression Management

Regardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situating it as part of team-based care delivery, and incorporating workflow concepts allowing for identification of shared decision-making tasks. We accomplish that by creating novel shared decision-making ontology which constitutes the first step required in the development of a decision support system for shared decision-making. The proposed ontology formally defines and describes the key concepts and relations in the shared decision-making domain and lays the foundation for the formalization and support of the patient management process. We illustrate the applicability of the proposed ontology by creating its instantiation for the complex patient management scenario involving shared decision-making about the treatment of metastatic spinal cord compression.

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.

An Environment for Guidelinebased Decision Support Systems for Outpatients Monitoring

Objectives: We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic.

Methods: We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2.

Results: The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient’s conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients.

Conclusions: Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients’ needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.

Decision support tools in low back pain

Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).

The development and feasibility of a personal health-optimization system for people with bipolar disorder

Background

People with bipolar disorder often experience ill health and have considerably reduced life expectancies. Suboptimal treatment is common and includes a lack of effective medicines, overtreatment, and non-adherence to medical interventions and lifestyle measures. E- and m-health applications support patients in optimizing their treatment but often exhibit conceptual and technical shortcomings. The objective of this work was to develop and test the usability of a system targeting suboptimal treatment and compare the service to other genres and strategies.

Methods

Based on the frameworks of shared decision-making, multi-criteria decision analysis, and single-subject research design, we interviewed potential users, reviewed research and current approaches, and created a first version using a rapid prototyping framework. We then iteratively improved and expanded the service based on formative usability testing with patients, healthcare providers, and laypeople from Norway, the UK, and Ukraine. The evidence-based health-optimization system was developed using systematic methods. The System Usability Scale and a questionnaire were administered in formative and summative tests. A comparison of the system to current standards for clinical practice guidelines and patient decision aids was performed.

Results

Seventy-eight potential users identified 82 issues. Driven by user feedback, the limited first version was developed into a more comprehensive system. The current version encompasses 21 integrated core features, supporting 6 health-optimization strategies. One crucial feature enables patients and clinicians to explore the likely value of treatments based on mathematical integration of self-reported and research data and the patient’s preferences. The mean ± SD (median) system usability score of the patient-oriented subsystem was 71 ± 18 (73). The mean ± SD (median) system usability score in the summative usability testing was 78 ± 18 (75), well above the norm score of 68. Feedback from the questionnaire was generally positive. Eighteen out of 23 components in the system are not required in international standards for patient decision aids and clinical practice guidelines.

Conclusion

We have developed the first evidence-based health-optimization system enabling patients, clinicians, and caregivers to collaborate in optimizing the patient’s health on a shared platform. User tests indicate that the feasibility of the system is acceptable.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0481-x) contains supplementary material, which is available to authorized users.

Patient and caregiver perspectives on decision support for symptom and quality of life management during cancer treatment: Implications for eHealth

OBJECTIVES

Adequate symptom and quality-of-life (SQL) management is a priority during cancer treatment. eHealth is a timely way to enhance patient-engagement, facilitate communication, and improve health outcomes. The objectives of this study were to describe patient and caregivers' perspectives for providing, processing, and managing SQL data to enhance communication and identify desired components for decision support.

METHODS

Data were collected from 64 participants through questionnaires and focus groups. Analysis was conducted using NVivo. Open and axial coding was completed, grouping commonalities and large constructs into nodes to identify and synthesize themes.

RESULTS

Face-to-face meetings with clinicians were the prime time to communicate, and patients strove to understand treatment options and the effect on SQL by bringing caregivers to their visits, taking notes, tracking symptoms, and creating portable health records. Patients/caregivers struggled to self-manage their symptoms and were uncertain when to contact clinicians when experiencing uncontrolled symptoms. Most participants identified eHealth solutions for decision support. However, 38% of participants (n = 24) rarely used computers and identified non-eHealth options for decision support. Core components for both eHealth and non-eHealth systems were access to (1) cancer information, (2) medical records, (3) peer support, and (4) improved support and understanding on when to contact clinicians.

CONCLUSIONS

Patients were faced with an overwhelming amount of information and relied on their caregivers to help navigate the complexities of cancer care and self-manage SQL. Health technologies can provide informational support; however, decision support needs to span multiple venues to avoid increasing disparities caused by a digital divide.

Using technology to engage hospitalised patients in their care: a realist review

BACKGROUND

Patient participation in health care is associated with improved outcomes for patients and hospitals. New technologies are creating vast potential for patients to participate in care at the bedside. Several studies have explored patient use, satisfaction and perceptions of health information technology (HIT) interventions in hospital. Understanding what works for whom, under what conditions, is important when considering interventions successfully engaging patients in care. This realist review aimed to determine key features of interventions using bedside technology to engage hospital patients in their care and analyse these in terms of context, mechanisms and outcomes.

METHODS

A realist review was chosen to explain how and why complex HIT interventions work or fail within certain contexts. The review was guided by Pawson's realist review methodology, involving: clarifying review scope; searching for evidence; data extraction and evidence appraisal; synthesising evidence and drawing conclusions. Author experience and an initial literature scope provided insight and review questions and theories (propositions) around why interventions worked were developed and iteratively refined. A purposive search was conducted to find evidence to support, refute or identify further propositions, which formed an explanatory model. Each study was 'mined' for evidence to further develop the propositions and model.

RESULTS

Interactive learning was the overarching theme of studies using technology to engage patients in their care. Several propositions underpinned this, which were labelled: information sharing; self-assessment and feedback; tailored education; user-centred design; and support in use of HIT. As studies were mostly feasibility or usability studies, they reported patient-centred outcomes including patient acceptability, satisfaction and actual use of HIT interventions. For each proposition, outcomes were proposed to come about by mechanisms including improved communication, shared decision-making, empowerment and self-efficacy; which acted as facilitators to patient participation in care. Overall, there was a stronger representation of health than IT disciplines in studies reviewed, with a lack of IT input in terms of theoretical underpinning, methodological design and reporting of outcomes.

CONCLUSION

HIT interventions have great potential for engaging hospitalised patients in their care. However, stronger interdisciplinary collaboration between health and IT researchers is needed for effective design and evaluation of HIT interventions.

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

OBJECTIVES

We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning.

METHODS

We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated.

RESULTS

A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992.

CONCLUSIONS

Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

Computerized Clinical Decision Support: Contributions from 2014

OBJECTIVE

To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.

METHOD

A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers.

RESULTS

Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy.

CONCLUSIONS

As health information technologies spread more and more meaningfully, CDSSs are improving to answer users' needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.