Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

The effects of cancer clinical decision support systems on patient-reported outcomes: A systematic review

PURPOSE

The implementation of high-quality decision-making support are integral to ensuring the delivery of quality cancer care and subsequently achieving positive patient outcomes. Decision Support Systems (DSS) are increasingly used, however it is not known what the effects are beyond supporting the decision-making process. We aimed to identify and synthesize the available literature regarding the effects of DSS on patient-reported outcomes both during and after cancer treatment.

METHODS

A systematic review was conducted using dual processes to identify empirical literature that reported an evaluation of DSS interventions and patient-reported outcomes. We appraised study quality using the Mixed Methods Appraisal Tool (MMAT). Data were narratively synthesized.

RESULTS

We included 15 studies, categorized as symptom assessment interventions or interactive educational interventions. Findings were mixed regarding the effectiveness of DSS interventions in improving total symptom distress and severity, whereas the majority were effective in reducing mean scores for worst and usual pain. Interventions were not effective in improving other health-related patient-reported outcomes including quality of life, global distress, depression, or self-efficacy and there were mixed effects for reducing decisional conflict. There was moderate to high patient adherence to the interventions and generally high satisfaction and acceptability, yet minimal evidence for the effect of DSS interventions in clinician adherence to intervention recommendations.

CONCLUSIONS

Including patient-reported outcomes in the evaluation of DSS is critical to understand their impact. Inconsistencies in reporting of interventions may, however, be a contributing factor to heterogeneous effects of clinical DSS regarding a broad range of patient-reported outcomes.

The Integration of Clinical Decision Support Systems Into Telemedicine for Patients With Multimorbidity in Primary Care Settings: Scoping Review

BACKGROUND

Multimorbidity, the presence of more than one condition in a single individual, is a global health issue in primary care. Multimorbid patients tend to have a poor quality of life and suffer from a complicated care process. Clinical decision support systems (CDSSs) and telemedicine are the common information and communication technologies that have been used to reduce the complexity of patient management. However, each element of telemedicine and CDSSs is often examined separately and with great variability. Telemedicine has been used for simple patient education as well as more complex consultations and case management. For CDSSs, there is variability in data inputs, intended users, and outputs. Thus, there are several gaps in knowledge about how to integrate CDSSs into telemedicine and to what extent these integrated technological interventions can help improve patient outcomes for those with multimorbidity.

OBJECTIVE

Our aims were to (1) broadly review system designs for CDSSs that have been integrated into each function of telemedicine for multimorbid patients in primary care, (2) summarize the effectiveness of the interventions, and (3) identify gaps in the literature.

METHODS

An online search for literature was conducted up to November 2021 on PubMed, Embase, CINAHL, and Cochrane. Searching from the reference lists was done to find additional potential studies. The eligibility criterion was that the study focused on the use of CDSSs in telemedicine for patients with multimorbidity in primary care. The system design for the CDSS was extracted based on its software and hardware, source of input, input, tasks, output, and users. Each component was grouped by telemedicine functions: telemonitoring, teleconsultation, tele-case management, and tele-education.

RESULTS

Seven experimental studies were included in this review: 3 randomized controlled trials (RCTs) and 4 non-RCTs. The interventions were designed to manage patients with diabetes mellitus, hypertension, polypharmacy, and gestational diabetes mellitus. CDSSs can be used for various telemedicine functions: telemonitoring (eg, feedback), teleconsultation (eg, guideline suggestions, advisory material provisions, and responses to simple queries), tele-case management (eg, sharing information across facilities and teams), and tele-education (eg, patient self-management). However, the structure of CDSSs, such as data input, tasks, output, and intended users or decision-makers, varied. With limited studies examining varying clinical outcomes, there was inconsistent evidence of the clinical effectiveness of the interventions.

CONCLUSIONS

Telemedicine and CDSSs have a role in supporting patients with multimorbidity. CDSSs can likely be integrated into telehealth services to improve the quality and accessibility of care. However, issues surrounding such interventions need to be further explored. These issues include expanding the spectrum of medical conditions examined; examining tasks of CDSSs, particularly for screening and diagnosis of multiple conditions; and exploring the role of the patient as the direct user of the CDSS.

Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review

BACKGROUND

Antibiotic resistance is a major health threat. Inappropriate antibiotic use has been shown to be an important determinant of the emergence of antibiotic resistance. Decision support systems for antimicrobial management can support clinicians to optimize antibiotic prescription.

OBJECTIVE

The aim of this systematic review is to identify factors influencing the implementation of decision support systems for antibiotic prescription in hospitals.

METHODS

A systematic search of factors impeding or facilitating successful implementation of decision support systems for antibiotic prescription was performed in January 2022 in the databases PubMed, Web of Science and The Cochrane Library. Only studies were included which comprised decision support systems in hospitals for prescribing antibiotic therapy, published in English with a qualitative, quantitative or mixed-methods study design and between 2011 and 2021. Factors influencing the implementation were identified through text analysis by two reviewers.

RESULTS

A total of 14 publications were identified matching the inclusion criteria. The majority of factors relate to technological and organizational aspects of decision support system implementation. Some factors include the integration of the decision support systems into existing systems, system design, consideration of potential end-users as well as training and support for end-users. In addition, user-related factors, like user attitude towards the system, computer literacy and prior experience with the system seem to be important for successful implementation of decision support systems for antibiotic prescription in hospitals.

CONCLUSION

The results indicate a broad spectrum of factors of decision support system implementation for antibiotic prescription and contributes to the literature by identifying important organizational as well as user-related factors. Wider organizational dimensions as well as the interaction between user and technology appear important for supporting implementation.

Is evidence of effectiveness a driver for clinical decision support selection? A qualitative descriptive study of senior hospital staff

Limited research has focused on understanding if and how evidence of health information technology (HIT) effectiveness drives the selection and implementation of technologies in practice. This study aimed to explore the views of senior hospital staff on the role evidence plays in the selection and implementation of HIT, with a particular focus on clinical decision support (CDS) alerts in electronic medication management systems. A qualitative descriptive design was used. Twenty senior hospital staff from six Australian hospitals in New South Wales and Queensland took part in a semistructured interview. Interviews were audio-recorded and transcribed, and a general inductive content analysis approach was used to identify themes. Participants acknowledged the importance of an evidence base, but reported that selection of CDS alerts, and HIT more broadly, was rarely underpinned by evidence that technologies improve patient care. Instead, investments in technologies were guided by the expectation that benefits will be achieved, bolstered by vendor assurances, and a perception that implementation of HIT is unavoidable. Postponing implementation of a technology until an evidence base is available was not always feasible. Although some technologies were seen as not requiring an evidence base, stakeholders viewed evidence as extremely valuable for informing decisions about selection of CDS alerts. In the absence of evidence, evaluation or monitoring of technologies postimplementation is critical, particularly to identify new errors or risks associated with HIT implementation and use. Increased transparency from vendors, with technology evaluation outcomes made directly available to healthcare organizations, may result in less reliance on logic, intuition, and vendor assertions and more evidence-based selection of HIT.

Clinical Decision Support for Glycemic Management Reduces Hospital Length of Stay

OBJECTIVE

Dysglycemia influences hospital outcomes and resource utilization. Clinical decision support (CDS) holds promise for optimizing care by overcoming management barriers. This study assessed the impact on hospital length of stay (LOS) of an alert-based CDS tool in the electronic medical record that detected dysglycemia or inappropriate insulin use, coined as gaps in care (GIC).

RESEARCH DESIGN AND METHODS

Using a 12-month interrupted time series among hospitalized persons aged ≥18 years, our CDS tool identified GIC and, when active, provided recommendations. We compared LOS during 6-month-long active and inactive periods using linear models for repeated measures, multiple comparison adjustment, and mediation analysis.

RESULTS

Among 4,788 admissions with GIC, average LOS was shorter during the tool's active periods. LOS reductions occurred for all admissions with GIC (-5.7 h, P = 0.057), diabetes and hyperglycemia (-6.4 h, P = 0.054), stress hyperglycemia (-31.0 h, P = 0.054), patients admitted to medical services (-8.4 h, P = 0.039), and recurrent hypoglycemia (-29.1 h, P = 0.074). Subgroup analysis showed significantly shorter LOS in recurrent hypoglycemia with three events (-82.3 h, P = 0.006) and nonsignificant in two (-5.2 h, P = 0.655) and four or more (-14.8 h, P = 0.746). Among 22,395 admissions with GIC (4,788, 21%) and without GIC (17,607, 79%), LOS reduction during the active period was 1.8 h (P = 0.053). When recommendations were provided, the active tool indirectly and significantly contributed to shortening LOS through its influence on GIC events during admissions with at least one GIC (P = 0.027), diabetes and hyperglycemia (P = 0.028), and medical services (P = 0.019).

CONCLUSIONS

Use of the alert-based CDS tool to address inpatient management of dysglycemia contributed to reducing LOS, which may reduce costs and improve patient well-being.

Stakeholder Perspectives on an Inpatient Hypoglycemia Informatics Alert: Mixed Methods Study

Background

Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event.

Objective

This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting.

Methods

Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches.

Results

A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants.

Conclusions

The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia.

Medical Specialists' Perspectives on the Influence of Electronic Medical Record Use on the Quality of Hospital Care: Semistructured Interview Study

Background

Numerous publications show that electronic medical records (EMRs) may make an important contribution to increasing the quality of care. There are indications that particularly the medical specialist plays an important role in the use of EMRs in hospitals.

Objective

The aim of this study was to examine how, and by which aspects, the relationship between EMR use and the quality of care in hospitals is influenced according to medical specialists.

Methods

To answer this question, a qualitative study was conducted in the period of August-October 2018. Semistructured interviews of around 90 min were conducted with 11 medical specialists from 11 different Dutch hospitals. For analysis of the answers, we used a previously published taxonomy of factors that can influence the use of EMRs.

Results

The professional experience of the participating medical specialists varied between 5 and 27 years. Using the previously published taxonomy, these medical specialists considered technical barriers the most significant for EMR use. The suboptimal change processes surrounding implementation were also perceived as a major barrier. A final major problem is related to the categories “social” (their relationships with the patients and fellow care providers), “psychological” (based on their personal issues, knowledge, and perceptions), and “time” (the time required to select, implement, and learn how to use EMR systems and subsequently enter data into the system). However, the medical specialists also identified potential technical facilitators, particularly in the assured availability of information to all health care professionals involved in the care of a patient. They see promise in using EMRs for medical decision support to improve the quality of care but consider these possibilities currently lacking.

Conclusions

The 11 medical specialists shared positive experiences with EMR use when comparing it to formerly used paper records. The fact that involved health care professionals can access patient data at any time they need is considered important. However, in practice, potential quality improvement lags as long as decision support cannot be applied because of the lack of a fully coded patient record.

A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing Choosing Wisely ® guidelines

Objective

To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS).

Materials and Methods

We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely^® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS.

Results

We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools.

Discussion

The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges.

Conclusion

Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.

Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Background

Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.

Aim

In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance.

Results

A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well‐trained end‐users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved.

Conclusion

We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

Supporting quality care for ESRD patients: the social worker can help address barriers to advance care planning

BACKGROUND

Advance Care Planning (ACP) is essential for preparation for end-of-life. It is a means through which patients clarify their treatment wishes. ACP is a patient-centered, dynamic process involving patients, their families, and caregivers. It is designed to 1) clarify goals of care, 2) increase patient agency over their care and treatments, and 3) help prepare for death. ACP is an active process; the end-stage renal disease (ESRD) illness trajectory creates health circumstances that necessitate that caregivers assess and nurture patient readiness for ACP discussions. Effective ACP enhances patient engagement and quality of life resulting in better quality of care.

MAIN BODY

Despite these benefits, ACP is not consistently completed. Clinical, technical, and social barriers result in key challenges to quality care. First, ACP requires caregivers to have end-of-life conversations that they lack the training to perform and often find difficult. Second, electronic health record (EHR) tools do not enable the efficient exchange of requisite psychosocial information such as treatment burden, patient preferences, health beliefs, priorities, and understanding of prognosis. This results in a lack of information available to enable patients and their families to understand the impact of illness and treatment options. Third, culture plays a vital role in end-of-life conversations. Social barriers include circumstances when a patient's cultural beliefs or value system conflicts with the caregiver's beliefs. Caregivers describe this disconnect as a key barrier to ACP. Consistent ACP is integral to quality patient-centered care and social workers' training and clinical roles uniquely position them to support ACP.

CONCLUSION

In this debate, we detail the known barriers to completing ACP for ESRD patients, and we describe its benefits. We detail how social workers, in particular, can support health outcomes by promoting the health information exchange that occurs during these sensitive conversations with patients, their family, and care team members. We aim to inform clinical social workers of this opportunity to enhance quality care by engaging in ACP. We describe research to help further elucidate barriers, and how researchers and caregivers can design and deliver interventions that support ACP to address this persistent challenge to quality end-of-life care.

Impact of Radiologist-Driven Change-Order Requests on Outpatient CT and MRI Examinations

PURPOSE

To assess impact of electronic medical record-embedded radiologist-driven change-order request on outpatient CT and MRI examinations.

METHODS

Outpatient CT and MRI requests where an order change was requested by the protocoling radiologist in our tertiary care center, from April 11, 2017, to January 3, 2018, were analyzed. Percentage and categorization of requested order change, provider acceptance of requested change, patient and provider demographics, estimated radiation exposure reduction, and cost were analyzed. P < .05 was used for statistical significance.

RESULTS

In 79,310 outpatient studies in which radiologists determined protocol, change-order requests were higher for MRI (5.2%, 1,283 of 24,553) compared with CT (2.9%, 1,585 of 54,757; P < .001). Provider approval of requested change was equivalent for CT (82%, 1,299 of 1,585) and MRI (82%, 1,052 of 1,283). Change requests driven by improper contrast media utilization were most common and different between CT (76%, 992 of 1,299) and MRI (65%, 688 of 1,052; P < .001). Changing without and with intravenous contrast orders to with contrast only was most common for CT (39%, 505 of 1,299) and with and without intravenous contrast to without contrast only was most common for MRI (26%, 274 of 1,052; P < .001). Of approved changes in CT, 51% (661 of 1,299) resulted in lower radiation exposure. Approved changes frequently resulted in less costly examinations (CT 67% [799 of 1,198], MRI 48% [411 of 863]).

CONCLUSION

Outpatient CT and MRI orders are deemed incorrect in 2.9% to 5% of cases. Radiologist-driven change-order request for CT and MRI are well accepted by ordering providers and decrease radiation exposure associated with imaging.

A patient-similarity-based model for diagnostic prediction

OBJECTIVE

To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically and predict diagnoses by the similar/dissimilar patients.

METHODS

We proposed a novel patient-similarity-based framework for diagnostic prediction, which is inspired by the structure-mapping theory about analogy reasoning in psychology. Patient similarity is defined as the similarity between two patients' diagnoses sets rather than a dichotomous (absence/presence of just one disease). The multilabel classification problem is converted to a single-value regression problem by integrating the pairwise patients' clinical features into a vector and taking the vector as the input and the patient similarity as the output. In contrast to the common k-NN method which only considering the nearest neighbors, we not only utilize similar patients (positive analogy) to generate diagnostic hypotheses, but also utilize dissimilar patients (negative analogy) are used to reject diagnostic hypotheses.

RESULTS

The patient-similarity-based models perform better than the one-vs-all baseline and traditional k-NN methods. The f-1 score of positive-analogy-based prediction is 0.698, significantly higher than the scores of baselines ranging from 0.368 to 0.661. It increases to 0.703 when the negative analogy method is applied to modify the prediction results of positive analogy. The performance of this method is highly promising for larger datasets.

CONCLUSION

The patient-similarity-based model provides diagnostic decision support that is more accurate, generalizable, and interpretable than those of previous methods and is based on heterogeneous and incomplete data. The model also serves as a new application for the use of clinical big data through artificial intelligence technology.

Precision health research and implementation reviewed through the conNECT framework

BACKGROUND

Precision health is a population-based approach that incorporates big-data strategies to understand the complex interactions between biological, environmental, lifestyle, and psychosocial factors that influence health.

PURPOSE

A promising tool to facilitate precision health research and its dissemination is the ConNECT Framework.

METHODS

Here, we discuss the relationship of the five broad and synergistic principles within the ConNECT Framework as they may apply to nursing science research: (1) Integrating Context, (2) Fostering a Norm of Inclusion, (3) Ensuring Equitable Diffusion of Innovations, (4) Harnessing Communication Technology, and (5) Prioritizing Specialized Training.

DISCUSSION/CONCLUSION

The principles within this framework can be used by nurse scientists and educators to guide and disseminate precision health research.

Indicator condition-guided HIV testing with an electronic prompt in primary healthcare: a before and after evaluation of an intervention

OBJECTIVE

Indicator condition (IC)-guided HIV testing is a strategy for the diagnosis of patients with HIV. The aim of this study was to assess the impact on the proportion of HIV tests requested after the introduction of an electronic prompt instructing primary healthcare (PHC) physicians to request an HIV test when diagnosing predefined IC.

METHODS

A prospective interventional study was conducted in 2015 in three PHC centres in Barcelona to assess the number of HIV test requests made during the implementation of an electronic prompt. Patients aged 18-65 years without HIV infection and with a new diagnosis of predefined IC were included. The results were compared with preprompt (2013) and postprompt data (2016).

RESULTS

During the prompt period, 832 patients presented an IC (median age 41.6 years [IQR 30-54], 48.2% female). HIV tests were requested in 296 individuals (35, 6%) and blood tests made in 238. Four HIV infections were diagnosed (positivity rate 1.7%, 95% CI 0.5% to 4.4%). The number of HIV tests requested based on IC increased from 12.6% in 2013 to 35.6% in 2015 (p<0.001) and fell to 17.9% after removal of the prompt in 2016 (p<0.001). Younger patient age (OR 0.97, 95% CI 0.96 to 0.98), birth outside Spain (OR 1.53, 95% CI 1.06 to 2.21) and younger physician age (OR 0.97, 95% CI 0.96 to 0.99) were independent predictive factors for an HIV test request during the prompt period. The electronic prompt (OR 3.36, 95% CI 2.70 to 4.18) was the factor most closely associated with HIV test requests. It was estimated that 10 (95% CI 3.0 to 26.2) additional new cases would have been diagnosed if an HIV test had been performed in all patients presenting an IC.

CONCLUSIONS

A significant increase in HIV test requests was observed during the implementation of the electronic prompt. The results suggest that this strategy could be useful in increasing IC-guided HIV testing in PHC centres.

Exploring patient–provider decision-making for use of anticoagulation for stroke prevention in atrial fibrillation: Results of the INFORM-AF study

Background:

Atrial fibrillation is associated with stroke, yet approximately 50% of patients are not treated with guideline-directed oral anticoagulants (OACs).

Aims:

Given that the etiology of this gap in care is not well understood, we explored decision-making by patients and physicians regarding OAC use for stroke prevention in atrial fibrillation.

Methods and results:

We conducted a descriptive qualitative study among providers ( N=28) and their patients with atrial fibrillation for whom OACs were indicated ( N=25). We used purposive sampling across three outpatient settings in which atrial fibrillation patients are commonly managed: primary care ( n=14), geriatrics ( n=10), and cardiology ( n=4). Eligible patients were stratified by those prescribed OAC ( n=13) and not prescribed OAC ( n=12). Semi-structured, in-depth interviews assessed decision-making regarding risk and OAC use. Classical content analysis was used to code narratives and identify themes. Results among patients consisted of the overarching theme of trust in provider recommendations. Sub-themes included: awareness of increased risk of stroke with atrial fibrillation; willingness to accept medications recommended by their physician; and low demand for explanatory decision aids. Among physicians, the overarching theme was decisional conflict regarding the balance between stroke and bleeding risk, and the optimal medication to prescribe. Subthemes included: absence of decision aids for communication; and misperceptions around the assessment and management of stroke risk with atrial fibrillation.

Conclusions:

Patient involvement in decision-making around OAC use did not occur in this study of patients with atrial fibrillation. Improved access to decision aids may increase patient engagement in the decision-making process of OAC use for stroke prevention.

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Introduction:  Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry.

Objective:  Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017.

Methods:  Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally.

Results:  CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions.

Conclusion:  Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.

Contributions from the 2016 Literature on Clinical Decision Support

Objectives: To summarize recent research and select the best papers published in 2016 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and section editor evaluation. Results: Among the 1,145 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper describes machine learning models used to predict breast cancer multidisciplinary team decisions and compares them with two predictors based on guideline knowledge. The second paper introduces a linked-data approach for publication, discovery, and interoperability of CDSSs. The third paper assessed the variation in high-priority drug-drug interaction (DDI) alerts across 14 Electronic Health Record systems, operating in different institutions in the US. The fourth paper proposes a generic framework for modeling multiple concurrent guidelines and detecting their recommendation interactions using semantic web technologies. Conclusions: The process of identifying and selecting best papers in the domain of CDSSs demonstrated that the research in this field is very active concerning diverse dimensions, such as the types of CDSSs, e.g. guideline-based, machine-learning-based, knowledge-fusion-based, etc., and addresses challenging areas, such as the concurrent application of multiple guidelines for comorbid patients, the resolution of interoperability issues, and the evaluation of CDSSs. Nevertheless, this process also showed that CDSSs are not yet fully part of the digitalized healthcare ecosystem. Many challenges remain to be faced with regard to the evidence of their output, the dissemination of their technologies, as well as their adoption for better and safer healthcare delivery.