Implementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation

BACKGROUND

Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes.

OBJECTIVE

The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization.

METHODS

An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics.

RESULTS

Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems.

CONCLUSION

The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review

BACKGROUND

Clinical decision support systems (CDSSs) have the potential to improve quality of care, patient safety, and efficiency because of their ability to perform medical tasks in a more data-driven, evidence-based, and semi-autonomous way. However, CDSSs may also affect the professional identity of health professionals. Some professionals might experience these systems as a threat to their professional identity, as CDSSs could partially substitute clinical competencies, autonomy, or control over the care process. Other professionals may experience an empowerment of the role in the medical system. The purpose of this study is to uncover the role of professional identity in CDSS implementation and to identify core human, technological, and organizational factors that may determine the effect of CDSSs on professional identity.

METHODS

We conducted a systematic literature review and included peer-reviewed empirical studies from two electronic databases (PubMed, Web of Science) that reported on key factors to CDSS implementation and were published between 2010 and 2023. Our explorative, inductive thematic analysis assessed the antecedents of professional identity-related mechanisms from the perspective of different health care professionals (i.e., physicians, residents, nurse practitioners, pharmacists).

RESULTS

One hundred thirty-one qualitative, quantitative, or mixed-method studies from over 60 journals were included in this review. The thematic analysis found three dimensions of professional identity-related mechanisms that influence CDSS implementation success: perceived threat or enhancement of professional control and autonomy, perceived threat or enhancement of professional skills and expertise, and perceived loss or gain of control over patient relationships. At the technological level, the most common issues were the system's ability to fit into existing clinical workflows and organizational structures, and its ability to meet user needs. At the organizational level, time pressure and tension, as well as internal communication and involvement of end users were most frequently reported. At the human level, individual attitudes and emotional responses, as well as familiarity with the system, most often influenced the CDSS implementation. Our results show that professional identity-related mechanisms are driven by these factors and influence CDSS implementation success. The perception of the change of professional identity is influenced by the user's professional status and expertise and is improved over the course of implementation.

CONCLUSION

This review highlights the need for health care managers to evaluate perceived professional identity threats to health care professionals across all implementation phases when introducing a CDSS and to consider their varying manifestations among different health care professionals. Moreover, it highlights the importance of innovation and change management approaches, such as involving health professionals in the design and implementation process to mitigate threat perceptions. We provide future areas of research for the evaluation of the professional identity construct within health care.

Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist

BACKGROUND

Computerized clinical decision support systems (CDSSs) can improve care by bridging knowledge to practice gaps. However, the real-world uptake of such systems in health care settings has been suboptimal. We sought to: (1) use the Theoretical Domains Framework (TDF) to identify determinants (barriers/enablers) of uptake of the Electronic Asthma Management System (eAMS) CDSS; (2) match identified TDF belief statements to elements in the Guideline Implementation with Decision Support (GUIDES) Checklist; and (3) explore the relationship between the TDF and GUIDES frameworks and the usefulness of this sequential approach for identifying opportunities to improve CDSS uptake.

METHODS

In Phase 1, we conducted semistructured interviews with primary care physicians in Toronto, Canada regarding the uptake of the eAMS CDSS. Using content analysis, two coders independently analyzed interview transcripts guided by the TDF to generate themes representing barriers and enablers to CDSS uptake. In Phase 2, the same reviewers independently mapped each belief statement to a GUIDES domain and factor. We calculated the proportion of TDF belief statements that linked to each GUIDES domain and the proportion of TDF domains that linked to GUIDES factors (and vice-versa) and domains.

RESULTS

We interviewed 10 participants before data saturation. In Phase 1, we identified 53 belief statements covering 12 TDF domains; 18 (34.0%) were barriers, and 35 (66.0%) were enablers. In Phase 2, 41 statements (77.4%) linked to at least one GUIDES factor, while 12 (22.6%) did not link to any specific factor. The GUIDES Context Domain was linked to the largest number of belief statements (19/53; 35.8%). Each TDF domain linked to one or more GUIDES factor, with 6 TDF domains linking to more than 1 factor and 8 TDF domains linking to more than 1 GUIDES domain.

CONCLUSIONS

The TDF provides unique insights into barriers and enablers to CDSS uptake, which can then be mapped to GUIDES domains and factors to identify required changes to CDSS context, content, and system. This can be followed by conventional mapping of TDF domains to behaviour change techniques to optimize CDSS implementation. This novel step-wise approach combines two established frameworks to optimize CDSS interventions, and requires prospective validation.

Examining primary care provider experiences with using a clinical decision support tool for pain management

Objective

To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation.

Materials and Methods

We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs. Using the 5 Rights of CDS framework, we conducted and analyzed semi-structured interviews with 19 PCPs across 2 academic health systems.

Results

PCPs stated that OneSheet mostly contained the right information required to treat patients with chronic pain and was correctly located in the electronic health record. PCPs used OneSheet for distinct subgroups of patients with chronic pain, including patients prescribed opioids, with poorly controlled pain, or new to a provider or clinic. PCPs reported variable workflow integration and selective use of certain OneSheet features driven by their preferences and patient population. PCPs recommended broadening OneSheet access to clinical staff and patients for data entry to address clinician time constraints.

Discussion

Differences in patient subpopulations and workflow preferences had an outsized effect on CDS tool use even when the CDS contained the right information identified in a user-centered design process.

Conclusions

To increase adoption and use, CDS design and implementation processes may benefit from increased tailoring that accommodates variation and dynamics among patients, visits, and providers.

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals

Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.

Healthcare Professionals’ Perceptions, Barriers, and Facilitators towards Adopting Computerised Clinical Decision Support Systems in Antimicrobial Stewardship in Jordanian Hospitals

Understanding healthcare professionals’ perceptions towards a computerised decision support system (CDSS) may provide a platform for the determinants of the successful adoption and implementation of CDSS. This cross-sectional study examined healthcare professionals’ perceptions, barriers, and facilitators to adopting a CDSS for antibiotic prescribing in Jordanian hospitals. This study was conducted among healthcare professionals in Jordan’s two tertiary and teaching hospitals over four weeks (June–July 2021). Data were collected in a paper-based format from senior and junior prescribers and non-prescribers (n = 254) who agreed to complete a questionnaire. The majority (n = 184, 72.4%) were aware that electronic prescribing and electronic health record systems could be used specifically to facilitate antibiotic use and prescribing. The essential facilitator made CDSS available in a portable format (n = 224, 88.2%). While insufficient training to use CDSS was the most significant barrier (n = 175, 68.9%). The female providers showed significantly lower awareness (p = 0.006), and the nurses showed significantly higher awareness (p = 0.041) about using electronic prescribing and electronic health record systems. This study examined healthcare professionals’ perceptions of adopting CDSS in antimicrobial stewardship (AMS) and shed light on the perceived barriers and facilitators to adopting CDSS in AMS, reducing antibiotic resistance, and improving patient safety. Furthermore, results would provide a framework for other hospital settings concerned with implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should concentrate on establishing policies and guidelines and a framework to examine the adoption of the CDSS for AMS.

Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review

BACKGROUND

Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow.

METHODS

A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed.

RESULTS

The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue".

CONCLUSIONS

The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.

Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review

PURPOSE

To identify and quantify the barriers and facilitators to the use of clinical decision support systems (CDSSs) by primary care professionals (PCPs).

METHODS

A mixed-methods systematic review was conducted using a sequential synthesis design. PubMed/MEDLINE, PsycInfo, Embase, CINAHL, and the Cochrane library were searched in July 2021. Studies that evaluated CDSSs providing recommendations to PCPs and intended for use during a consultation were included. We excluded CDSSs used only by patients, described as concepts or prototypes, used with simulated cases, and decision supports not considered as CDSSs. A framework synthesis was performed according to the HOT-fit framework (Human, Organizational, Technology, Net Benefits), then a quantitative synthesis evaluated the impact of the HOT-fit categories on CDSS use.

RESULTS

A total of 48 studies evaluating 45 CDSSs were included, and 186 main barriers or facilitators were identified. Qualitatively, barriers and facilitators were classified as human (eg, perceived usefulness), organizational (eg, disruption of usual workflow), and technological (eg, CDSS user-friendliness), with explanatory elements. The greatest barrier to using CDSSs was an increased workload. Quantitatively, the human and organizational factors had negative impacts on CDSS use, whereas the technological factor had a neutral impact and the net benefits dimension a positive impact.

CONCLUSIONS

Our findings emphasize the need for CDSS developers to better address human and organizational issues, in addition to technological challenges. We inferred core CDSS features covering these 3 factors, expected to improve their usability in primary care.

The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review

A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow.

Acceptance of clinical decision support systems in Saudi healthcare organisations

Applications of clinical decision support systems (CDSS) have become essential for physicians seeking to make appropriate decisions. The implementation of CDSS, however, is complicated if the factors affecting physician’s acceptance are not recognised. This study aims to explore the various factors that may influence the acceptance of CDSS in Saudi Arabia. A qualitative method was used to collect data from interviews with 54 GPs, with interviews conducted in three stages. The study then integrated the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to communicate the findings. It is suggested that all factors of both UTAUT and TTF influence acceptance of CDSS by GPs, with the sole exception of the social influence factor. Some additional factors were also discovered by means of in-depth interviews, including accessibility, patient satisfaction, informativeness (increased knowledge), connectedness (informing patients), communication and shared knowledge, privacy and security, and perceived risk (functional performance risk and time risk). The study thus offers a new insight of the factors influencing GPs’ acceptance of CDSS.

Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator

OBJECTIVE

Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outputs will need to be understood and trusted by physicians, and then explained to patients. We therefore investigated the association between physician understanding of ML outputs, their ability to explain these to patients, and their willingness to trust the ML outputs, using various ML explainability methods.

MATERIALS AND METHODS

We designed a survey for physicians with a diagnostic dilemma that could be resolved by an ML risk calculator. Physicians were asked to rate their understanding, explainability, and trust in response to 3 different ML outputs. One ML output had no explanation of its logic (the control) and 2 ML outputs used different model-agnostic explainability methods. The relationships among understanding, explainability, and trust were assessed using Cochran-Mantel-Haenszel tests of association.

RESULTS

The survey was sent to 1315 physicians, and 170 (13%) provided completed surveys. There were significant associations between physician understanding and explainability (P < .001), between physician understanding and trust (P < .001), and between explainability and trust (P < .001). ML outputs that used model-agnostic explainability methods were preferred by 88% of physicians when compared with the control condition; however, no particular ML explainability method had a greater influence on intended physician behavior.

CONCLUSIONS

Physician understanding, explainability, and trust in ML risk calculators are related. Physicians preferred ML outputs accompanied by model-agnostic explanations but the explainability method did not alter intended physician behavior.

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management

The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.

Reduction of inappropriate medication in older populations by electronic decision support (the PRIMA-eDS project): a survey of general practitioners' experiences

Objective

We sought to investigate the experiences of general practitioners (GPs) with an electronic decision support tool to reduce inappropriate polypharmacy in older patients (the PRIMA-eDS [Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support] tool) in a multinational sample of GPs and to quantify the findings from a prior qualitative study on the PRIMA-eDS-tool.

Materials and Methods

Alongside the cluster randomized controlled PRIMA-eDS trial, a survey was conducted in all 5 participating study centers (Bolzano, Italy; Manchester, United Kingdom; Salzburg, Austria; Rostock, Germany; and Witten, Germany) between October 2016 and July 2017. Data were analyzed using descriptive statistics and chi-square tests.

Results

Ninety-one (n = 160) percent of the 176 questionnaires were returned. Thirty-two percent of the respondents reported that they did not cease drugs because of the medication check. The 68% who had discontinued drugs comprise 57% who had stopped on average 1 drug and 11% who had stopped 2 drugs or more per patient. The PRIMA-eDS tool was found to be useful (69%) and the recommendations were found to help to increase awareness (86%). The greatest barrier to implementing deprescribing recommendations was the perceived necessity of the medication (69%). The majority of respondents (65%) would use the electronic medication check in routine practice if it was part of the electronic health record.

Conclusions

GPs generally viewed the PRIMA-eDS medication check as useful and as informative. Recommendations were not always followed due to various reasons. Many GPs would use the medication check if integrated into the electronic health record.

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department

OBJECTIVE

The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.

METHODS

During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking.

RESULTS

Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking.

CONCLUSIONS

To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.

Effect of a patient-centred deprescribing procedure in older multimorbid patients in Swiss primary care - A cluster-randomised clinical trial

Background

Management of patients with polypharmacy is challenging, and evidence for beneficial effects of deprescribing interventions is mixed. This study aimed to investigate whether a patient-centred deprescribing intervention of PCPs results in a reduction of polypharmacy, without increasing the number of adverse disease events and reducing the quality of life, among their older multimorbid patients.

Methods

This is a cluster-randomised clinical study among 46 primary care physicians (PCPs) with a 12 months follow-up. We randomised PCPs into an intervention and a control group. They recruited 128 and 206 patients if ≥60 years and taking ≥five drugs for ≥6 months. The intervention consisted of a 2-h training of PCPs, encouraging the use of a validated deprescribing-algorithm including shared-decision-making, in comparison to usual care. The primary outcome was the mean difference in the number of drugs per patient (dpp) between baseline and after 12 months. Additional outcomes focused on patient safety and quality of life (QoL) measures.

Results

Three hundred thirty-four patients, mean [SD] age of 76.2 [8.5] years participated. The mean difference in the number of dpp between baseline and after 12 months was 0.379 in the intervention group (8.02 and 7.64; p = 0.059) and 0.374 in the control group (8.05 and 7.68; p = 0.065). The between-group comparison showed no significant difference at all time points, except for immediately after the intervention (p = 0.002). There were no significant differences concerning patient safety nor QoL measures.

Conclusion

Our straight-forward and patient-centred deprescribing procedure is effective immediately after the intervention, but not after 6 and 12 months. Further research needs to determine the optimal interval of repeated deprescribing interventions for a sustainable effect on polypharmacy at mid- and long-term. Integrating SDM in the deprescribing process is a key factor for success.

Trial registration

Current Controlled Trials, prospectively registered ISRCTN16560559 Date assigned 31/10/2014.

The Prevention of Polypharmacy in Primary Care Patients Trial (4P-RCT).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-020-01870-8.

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Objective

The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.

Materials and methods

Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations.

Results

Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters.

Conclusion

In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications.

Key Points

• Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians.

• Implementation of AI in radiology is facilitated by the presence of a local champion.

• Evidence on the clinical added value of AI in radiology is needed for successful implementation.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users.

Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow

The overwhelming number of medication alerts generated by clinical decision support systems (CDSS) has led to inappropriate alert overrides, which may lead to unintended patient harm. This review highlights the factors affecting the alert appropriateness of CDSS and barriers to the fit of CDSS alert with clinical workflow. A literature review was conducted to identify features and functions pertinent to CDSS alert appropriateness using the five rights of CDSS. Moreover, a process improvement method, namely, Lean, was used as a tool to optimise clinical workflows, and the appropriate design for CDSS alert using a human automation interaction (HAI) model was recommended. Evaluating the appropriateness of CDSS alert and its impact on workflow provided insights into how alerts can be designed and triggered effectively to support clinical workflow. The application of Lean methods and tools to analyse alert efficiencies in supporting workflow in this study provides an in-depth understanding of alert-workflow fit problems and their root cause, which is required for improving CDSS design. The application of the HAI model is recommended in the design of CDSS alerts to support various levels and stages of alert automations, namely, information acquisition and analysis, decision action and action implementation.

Association between GP participation in a primary care group and monitoring of biomedical and lifestyle target indicators in people with type 2 diabetes: a cohort study (ELZHA cohort-1)

OBJECTIVE

Whether care group participation by general practitioners improves delivery of diabetes care is unknown. Using 'monitoring of biomedical and lifestyle target indicators as recommended by professional guidelines' as an operationalisation for quality of care, we explored whether (1) in new practices monitoring as recommended improved a year after initial care group participation (aim 1); (2) new practices and experienced practices differed regarding monitoring (aim 2).

DESIGN

Observational, real-life cohort study.

SETTING

Primary care registry data from Eerstelijns Zorggroep Haaglanden (ELZHA) care group.

PARTICIPANTS

Aim 1: From six new practices (n=538 people with diabetes) that joined care group ELZHA in January 2014, two practices (n=211 people) were excluded because of missing baseline data; four practices (n=182 people) were included. Aim 2: From all six new practices (n=538 people), 295 individuals were included. From 145 experienced practices (n=21 465 people), 13 744 individuals were included.

EXPOSURE

Care group participation includes support by staff nurses on protocolised diabetes care implementation and availability of a system providing individual monitoring information. 'Monitoring as recommended' represented minimally one annual registration of each biomedical (HbA1c, systolic blood pressure, low-density lipoprotein) and lifestyle-related target indicator (body mass index, smoking behaviour, physical exercise).

PRIMARY OUTCOME MEASURES

Aim 1: In new practices, odds of people being monitored as recommended in 2014 were compared with baseline (2013). Aim 2: Odds of monitoring as recommended in new and experienced practices in 2014 were compared.

RESULTS

Aim 1: After 1-year care group participation, odds of being monitored as recommended increased threefold (OR 3.00, 95% CI 1.84 to 4.88, p<0.001). Aim 2: Compared with new practices, no significant differences in the odds of monitoring as recommended were found in experienced practices (OR 1.21, 95% CI 0.18 to 8.37, p=0.844).

CONCLUSIONS

We observed a sharp increase concerning biomedical and lifestyle monitoring as recommended after 1-year care group participation, and subsequently no significant difference between new and experienced practices-indicating that providing diabetes care within a collective approach rapidly improves registration of care.

Do computerized clinical decision support systems improve the prescribing of oral anticoagulants? A systematic review

BACKGROUND

Serious adverse drug reactions have been associated with the underuse or the misuse of oral anticoagulant therapy. We systematically reviewed the impact of computerized clinical decision support systems (CDSS) on the prescribing of oral anticoagulants and we described CDSS features associated with success or failure.

METHODS

We searched Medline, Embase, CENTRAL, CINHAL, and PsycINFO for studies that compared CDSS for the initiation or monitoring of oral anticoagulants with routine care. Two reviewers performed study selection, data collection, and risk-of-bias assessment. Disagreements were resolved with a third reviewer. Potentially important CDSS features, identified from previous literature, were evaluated.

RESULTS

Sixteen studies were included in our qualitative synthesis. Most trials were performed in primary care (n = 7) or hospitals (n = 6) and included atrial fibrillation (AF) patients (n = 9). Recommendations mainly focused on anticoagulation underuse (n = 11) and warfarin-drug interactions (n = 5). Most CDSS were integrated in electronic records or prescribing and provided support automatically at the time and location of decision-making. Significant improvements in practitioner performance were found in 9 out of 16 studies, while clinical outcomes were poorly reported. CDSS features seemed slightly more common in studies that demonstrated improvement.

CONCLUSIONS

CDSS might positively impact the use of oral anticoagulants in AF patients at high risk of stroke. The scope of CDSS should now evolve to assist prescribers in selecting the most appropriate and tailored medication. Efforts should nevertheless be made to improve the relevance of notifications and to address implementation outcomes.

Telephone nurses' use of a decision support system: An observational study

Telephone nurses give advice and support and make assessments based on verbal communication only. Web-based decision support systems are often used to increase patient safety and make medically correct assessments. The aim of the present this study was to describe factors affecting the use of a decision support system and experiences with this system among telephone nurses in Swedish primary health care. Observations and semistructured interviews were conducted. Six registered nurses with at least 1 year of experience of telephone nursing participated. Field notes and interviews were analyzed by qualitative content analysis. The main findings of the present this study were factors that decrease the decision support system use or promote deviation from decision support system use, factors that are positive for decision support system use and the decision support system complicates the work. Underuse and deviations from decision support systems can be a safety risk, because decisions are based on too little information. Further research with observations of telephone nurses' use of decision support systems is needed to develop both telephone nursing and decision support systems.

What drives adoption of a computerised, multifaceted quality improvement intervention for cardiovascular disease management in primary healthcare settings? A mixed methods analysis using normalisation process theory

Background

A computerised, multifaceted quality improvement (QI) intervention for cardiovascular disease (CVD) management in Australian primary healthcare was evaluated in a cluster randomised controlled trial. The intervention was associated with improved CVD risk factor screening but there was no improvement in prescribing rates of guideline-recommended medicines. The aim of this study was to conduct a process evaluation to identify and explain the underlying mechanisms by which the intervention did and did not have an impact.

Methods/design

Normalisation process theory (NPT) was used to understand factors that supported or constrained normalisation of the intervention into routine practice. A case study design was used in which six of the 30 participating intervention sites were purposively sampled to obtain a mix of size, governance, structure and performance. Multiple data sources were drawn on including trial outcome data, surveys of job satisfaction and team climate (68 staff) and in-depth interviews (19 staff). Data were primarily analysed within cases and compared with quantitative findings in other trial intervention and usual care sites.

Results

We found a complex interaction between implementation processes and several contextual factors affecting uptake of the intervention. There was no clear association between team climate, job satisfaction and intervention outcomes. There were four spheres of influence that appeared to enhance or detract from normalisation of the intervention: organisational mission and history (e.g. strategic investment to promote a QI culture enhanced cognitive participation), leadership (e.g. ability to energise or demotivate others influenced coherence), team environment (e.g. synergistic activities of team members with different skill sets influenced collective action) and technical integrity of the intervention (e.g. tools that slowed computer systems limited reflective action).

Discussion

Use of NPT helped explain how certain contextual factors influence the work that is done by individuals and teams when implementing a novel intervention. Although these factors do not necessarily distil into a recipe for successful uptake, they may assist system planners, intervention developers, and health professionals to better understand the trajectory that primary health care services may take when developing and engaging with QI interventions.

Trial registration

ACTRN 12611000478910. Registered 08 May 2011.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0830-x) contains supplementary material, which is available to authorized users.

A training programme facilitating guideline use of occupational health professionals: a feasibility study

BACKGROUND

To evaluate whether a training programme is a feasible approach to facilitate occupational health professionals' (OHPs) use of knowledge and skills provided by a guideline.

METHODS

Feasibility was evaluated by researching three aspects: 'acceptability', 'implementation' and 'limited efficacy'. Statements on acceptability and implementation were rated by OHPs on 10-point visual analogue scales after following the training programme (T2). Answers were analysed using descriptive statistics. Barriers to and facilitators of implementation were explored through open-ended questions at T2, which were qualitatively analysed. Limited efficacy was evaluated by measuring the level of knowledge and skills at baseline (T0), after reading the guideline (T1) and directly after completing the training programme (T2). Increase in knowledge and skills was analysed using a non-paramatric Friedman test and post-hoc Wilcoxon signed rank tests (two-tailed).

RESULTS

The 38 OHPs found the training programme acceptable, judging that it was relevant (M: 8, SD: 1), increased their capability (M: 7, SD: 1), adhered to their daily practice (M: 8, SD: 1) and enhanced their guidance and assessment of people with a chronic disease (M: 8, SD: 1). OHPs found that it was feasible to implement the programme on a larger scale (M: 7, SD: 1) but foresaw barriers such as 'time', 'money' and organizational constraints. The reported facilitators were primarily related to the added value of the knowledge and skills to the OHPs' guidance and assessment, and that the programme taught them to apply the evidence in practice. Regarding limited efficacy, a significant increase was seen in OHPs' knowledge and skills over time (X² (2) = 53.656, p < 0.001), with the median score improving from 6.3 (T0), 8.3 (T1) and 12.3 (T2). Post-hoc tests indicated a significant improvement between T0 and T1 (p < 0.001) and between T1 and T2 (p < 0.001).

CONCLUSIONS

The training programme was found to be a feasible approach to facilitate OHPs' use of knowledge and skills provided by the guideline, from the perspective of OHPs generally (acceptability and implementation) and with respect to their increase in knowledge and skills in particular (limited efficacy).

Effectiveness of CHA2DS2-VASc based decision support on stroke prevention in atrial fibrillation: A cluster randomised trial in general practice

BACKGROUND

Guidelines on atrial fibrillation (AF) recommend the CHA₂DS₂-VASc rule for anticoagulant decision-making, but underuse exists. We studied the impact of an automated decision support on stroke prevention in patients with AF in a cluster randomised trial in general practice.

METHODS

Intervention practices were provided with a CHA₂DS₂-VASc based anticoagulant treatment recommendation. Reference practices provided care as usual. The primary outcome was incidence of ischaemic stroke, transient ischaemic attack (TIA) and/or thromboembolism (TE). Secondary outcomes were bleeding and the proportion of patients on guideline recommended anticoagulant treatment.

RESULTS

In total, 1129 AF patients were included in the 19 intervention practices and 1226 AF patients in the 19 reference practices. The median age was 77 (interquartile range (IQR) 68-75) years, the median CHA₂DS₂-VASc score was 3.0 (IQR 2.0-5.0). Underuse of anticoagulants in patients with CHA₂DS₂-VASc score ≥ 2 was 6.6%. After a median follow-up of 2.7 years (IQR 2.3-3.0), the incidence rate per 100 person-years of ischaemic stroke/TIA/TE was 1.96 in the intervention group and 1.42 in the reference group (hazard ratio (HR) 1.3, 95% C.I. 0.8-2.1). No difference was observed in the rate of bleeding (0.79 versus 0.82), or in the underuse (7.2% versus 8.2%) or overuse (8.0% versus 7.9%) of anticoagulation.

CONCLUSIONS

In this study in patients with AF in general practice, underuse of anticoagulants was relatively low. Providing practitioners with CHA₂DS₂-VASc based decision support did not result in a reduction in stroke incidence, affect bleeding risk or anticoagulant over- or underuse.

Reduction of inappropriate medication in older populations by electronic decision support (the PRIMA-eDS study): a qualitative study of practical implementation in primary care

BACKGROUND

Within the EU-funded project PRIMA-eDS (Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support) an electronic decision support tool (the "PRIMA-eDS-tool") was developed for general practitioners (GPs) to reduce inappropriate medication in their older polypharmacy patients. After entering patient data relevant to prescribing in an electronic case report form the physician received a comprehensive medication review (CMR) on his/her screen displaying recommendations regarding missing indications, necessary laboratory tests, evidence-base of current medication, dose adjustments for renal malfunction, potentially harmful drug-drug interactions, contra-indications, and possible adverse drug events. We set out to explore the usage of the PRIMA-eDS tool and the adoption of the recommendations provided by the CMR to optimise the tool and prepare it for its future implementation.

METHODS

In a qualitative study carried out in North Rhine-Westphalia, Germany, 21 GPs using the PRIMA-eDS tool within the PRIMA-eDS study were interviewed. Interviews encompassed the GPs' attitudes regarding use of the electronic case report form and the CMR, their response to the recommendations, and the implementation of the tool into daily practice routine. The collected data were analysed applying thematic qualitative text analysis.

RESULTS

GPs found the patient data entry into the electronic case report form to be inconvenient and time-consuming. The CMR was conducted often outside practice hours and without the patient present. GPs found that the PRIMA-eDS CMR provided relevant information for and had several positive effects on the caring process. However, they encountered several barriers when wanting to change medication.

CONCLUSIONS

It is unlikely that the PRIMA-eDS CMR will be used in the future as it is now as patient data entry is too time-consuming. Several barriers towards deprescribing medications were found which are common in deprescribing studies. Given the positive attitude towards the CMR, a new way of entering patient data into the PRIMA-eDS tool to create the CMR needs to be developed.

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed.

Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully.

Methods

We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist.

Results

We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy.

Conclusions

The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0772-3) contains supplementary material, which is available to authorized users.

Patient and provider perspectives on uptake of a shared decision making intervention for asthma in primary care practices

OBJECTIVE

Poor outcomes and health disparities related to asthma result in part from difficulty disseminating new evidence such as shared decision making (SDM) into clinical practice. As part of a three-arm cluster randomized dissemination study, evaluation of the impact of different dissemination methods was studied. Here we evaluate themes from patient and provider focus groups to assess the impact of a facilitated, traditional dissemination approach, or no intervention, on patient and provider perspectives of asthma care.

METHODS

Using semi-structured questions, twenty-four pre- and post-intervention focus groups with patients and providers took place across primary care practices. Discussions were held in all three arms both before and after the time of intervention rollout. Audio recordings were transcribed and analyzed for themes.

RESULTS

Across all sites patients and providers discussed themes of communication, asthma self-management, barriers, education, and patient awareness. After the intervention, compared to traditional sites, facilitated practices were more likely to discuss themes related to SDM, such as patient-centered communication, patient-provider negotiation on treatment plan, planning, goal-setting, and solutions to barriers.

CONCLUSIONS

Emergent themes allowed for further understanding of how the SDM implementation was perceived at the patient and provider level. The facilitated implementation was associated with higher adoption of the SDM intervention. These themes and supporting quotes add to knowledge of best practices associated with implementing an evidence-based SDM intervention for asthma into primary care and will inform researchers, practices, and providers as they work to improve adoption of evidence-based interventions into practice.

Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis

Objective

To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research.

Materials and Methods

We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research.

Results

The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process.

Discussion

The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations.

Conclusion

The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.

Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: A mixed methods evaluation

Background

Many studies have investigated the use of clinical decision support systems as a means to improve care, but have thus far failed to show significant effects on patient-related outcomes. We developed a clinical decision support system that attempted to address issues that were identified in these studies. The system was implemented in Dutch general practice and was designed to be both unobtrusive and to respond in real time. Despite our efforts, usage of the system was low. In the current study we perform a mixed methods evaluation to identify remediable barriers which led to disappointing usage rates for our system.

Methods

A mixed methods evaluation employing an online questionnaire and focus group. The focus group was organized to clarify free text comments and receive more detailed feedback from general practitioners. Topics consisted of items based on results from the survey and additional open questions.

Results

The response rate for the questionnaire was 94%. Results from the questionnaire and focus group can be summarized as follows: The system was perceived as interruptive, despite its design. Participants felt that there were too many recommendations and that the relevance of the recommendations varied. Demographic based recommendations (e.g. age) were often irrelevant, while specific risk-based recommendations (e.g. diagnosis) were more relevant. The other main barrier to use was lack of time during the patient visit.

Conclusion

These results are likely to be useful to other researchers who are attempting to address the problems of interruption and alert fatigue in decision support.

What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation

BACKGROUND

Advanced Computerized Decision Support Systems (CDSSs) assist clinicians in their decision-making process, generating recommendations based on up-to-date scientific evidence. Although this technology has the potential to improve the quality of patient care, its mere provision does not guarantee uptake: even where CDSSs are available, clinicians often fail to adopt their recommendations. This study examines the barriers and facilitators to the uptake of an evidence-based CDSS as perceived by diverse health professionals in hospitals at different stages of CDSS adoption.

METHODS

Qualitative study conducted as part of a series of randomized controlled trials of CDSSs. The sample includes two hospitals using a CDSS and two hospitals that aim to adopt a CDSS in the future. We interviewed physicians, nurses, information technology staff, and members of the boards of directors (n = 30). We used a constant comparative approach to develop a framework for guiding implementation.

RESULTS

We identified six clusters of experiences of, and attitudes towards CDSSs, which we label as "positions." The six positions represent a gradient of acquisition of control over CDSSs (from low to high) and are characterized by different types of barriers to CDSS uptake. The most severe barriers (prevalent in the first positions) include clinicians' perception that the CDSSs may reduce their professional autonomy or may be used against them in the event of medical-legal controversies. Moving towards the last positions, these barriers are substituted by technical and usability problems related to the technology interface. When all barriers are overcome, CDSSs are perceived as a working tool at the service of its users, integrating clinicians' reasoning and fostering organizational learning.

CONCLUSIONS

Barriers and facilitators to the use of CDSSs are dynamic and may exist prior to their introduction in clinical contexts; providing a static list of obstacles and facilitators, irrespective of the specific implementation phase and context, may not be sufficient or useful to facilitate uptake. Factors such as clinicians' attitudes towards scientific evidences and guidelines, the quality of inter-disciplinary relationships, and an organizational ethos of transparency and accountability need to be considered when exploring the readiness of a hospital to adopt CDSSs.

Effectiveness and usage of a decision support system to improve stroke prevention in general practice: A cluster randomized controlled trial

BACKGROUND

Adherence to guidelines pertaining to stroke prevention in patients with atrial fibrillation is poor. Decision support systems have shown promise in increasing guideline adherence.

AIMS

To improve guideline adherence with a non-obtrusive clinical decision support system integrated in the workflow. Secondly, we seek to capture reasons for guideline non-adherence.

DESIGN AND SETTING

A cluster randomized controlled trial in Dutch general practices.

METHOD

A decision support system was developed that implemented properties positively associated with effectiveness: real-time, non-interruptive and based on data from electronic health records. Recommendations were based on the Dutch general practitioners guideline for atrial fibrillation that uses the CHA2DS2-VAsc for stroke risk stratification. Usage data and responses to the recommendations were logged. Effectiveness was measured as adherence to the guideline. We used a chi square to test for group differences and a mixed effects model to correct for clustering and baseline adherence.

RESULTS

Our analyses included 781 patients. Usage of the system was low (5%) and declined over time. In total, 76 notifications received a response: 58% dismissal and 42% acceptance. At the end of the study, both groups had improved, by 8% and 5% respectively. There was no statistically significant difference between groups (Control: 50%, Intervention: 55% P = 0.23). Clustered analysis revealed similar results. Only one usable reasons for non-adherence was captured.

CONCLUSION

Our study could not demonstrate the effectiveness of a decision support system in general practice, which was likely due to lack of use. Our findings should be used to develop next generation decision support systems that are effective in the challenging setting of general practice.

Assessment and improvement of HIV screening rates in a Midwest primary care practice using an electronic clinical decision support system: a quality improvement study

Background

Universal human immunodeficiency virus (HIV) screening remains low in many clinical practices despite published guidelines recommending screening for all patients between ages 13–65. Electronic clinical decision support tools have improved screening rates for many chronic diseases. We designed a quality improvement project to improve the rate of universal HIV screening of adult patients in a Midwest primary care practice using a clinical decision support tool.

Methods

We conducted this quality improvement project in Rochester, Minnesota from January 1, 2014 to December 31, 2014. Baseline primary care practice HIV screening data were acquired from January 1, 2014 to April 30, 2014. We surveyed providers and educated them about current CDC recommended screening guidelines. We then added an HIV screening alert to an existing electronic clinical decision support tool and post-intervention HIV screening rates were obtained from May 1, 2014 to December 31, 2014. The primary quality outcome being assessed was change in universal HIV screening rates.

Results

Twelve thousand five hundred ninety-six unique patients were eligible for HIV screening in 2014; 327 were screened for HIV. 6,070 and 6,526 patients were seen before and after the intervention, respectively. 1.80 % of eligible patients and 3.34 % of eligible patients were screened prior to and after the intervention, respectively (difference of −1.54 % [−2.1 %, −0.99 %], p < 0.0001); OR 1.89 (1.50, 2.38). Prior to the intervention, African Americans were more likely to have been screened for HIV (OR 3.86 (2.22, 6.71; p < 0.001) than Whites, but this effect decreased significantly after the intervention (OR 1.90 (1.12, 3.21; p = 0.03).

Conclusions

These data showed that an electronic alert almost doubled the rates of universal HIV screening by primary care providers in a Midwestern practice and reduced racial disparities, but there is still substantial room for improvement in universal screening practices. Opportunities for universal HIV screening remain abundant, as many providers either do not understand the importance of screening average risk patients or do not remember to discuss it. Alerts to remind providers of current guidelines and help identify screening opportunities can be helpful.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0320-5) contains supplementary material, which is available to authorized users.

Exposure to and experiences with a computerized decision support intervention in primary care: results from a process evaluation

Background

Trials evaluating the effects of interventions usually provide little insight into the factors responsible for (lack of) changes in desired outcomes. A process evaluation alongside a trial can shed light on the mechanisms responsible for the outcomes of a trial. The aim of this study was to investigate exposure to and experiences with a computerized decision support system (CDSS) intervention, in order to gain insight into the intervention’s impact and to provide suggestions for improvement.

Methods

A process evaluation was conducted as part of a large-scale cluster-randomized controlled trial investigating the effects of the CDSS NHGDoc on quality of care. Data on exposure to and experiences with the intervention were collected during the trial period among participants in both the intervention and control group - whenever applicable - by means of the NHGDoc server and an electronic questionnaire. Multiple data were analyzed using descriptive statistics.

Results

Ninety-nine percent (n = 229) of the included practices generated data for the NHGDoc server and 50 % (n = 116) responded to the questionnaire: both general practitioners (GPs; n = 112; 49 %) and practice nurses (PNs; n = 52; 37 %) participated. The actual exposure to the NHGDoc system and specific heart failure module was limited with 52 % of the GPs and 42 % of the PNs reporting to either never or rarely use the system. Overall, users had a positive attitude towards CDSSs. The most perceived barriers to using NHGDoc were a lack of learning capacity of the system, the additional time and work it requires to use the CDSS, irrelevant alerts, too high intensity of alerts and insufficient knowledge regarding the system.

Conclusions

Several types of barriers may have negatively affected the impact of the intervention. Although users are generally positive about CDSSs, a large share of them is insufficiently aware of the functions of NHGDoc and, finds the decision support not always useful or relevant and difficult to integrate into daily practice. In designing CDSS interventions we suggest to more intensely involve the end-users and increase the system’s flexibility and learning capacity. To improve implementation a proper introduction of a CDSS among its target group including adequate training is advocated.

Trial registration

Clinical trials NCT01773057.

Electronic supplementary material

The online version of this article (doi:10.1186/s12875-015-0364-0) contains supplementary material, which is available to authorized users.