Adoption Of Clinical Decision Support Systems Research Articles

CDSS Adoption and the Role of Artificial Intelligence in Saudi Arabian Primary Healthcare

The integration of advanced Clinical Decision Support Systems (CDSS) is crucial in healthcare, especially in developing countries. However, the challenges to CDSS implementation in these settings are often overlooked, leading to implementation gaps. This study investigates the adoption of CDSS in primary healthcare facilities in Saudi Arabia through in-depth interviews with general practitioners. The advancement of primary healthcare systems in developing nations necessitates appropriate technological solutions to address issues such as equitable access, service quality, and patient satisfaction. The adoption of CDSS can potentially improve patient satisfaction and enhance medical consultations. Artificial intelligence -driven technologies like the Velys robot are transforming the medical field by assisting doctors in surgical procedures. In healthcare, Artificial intelligence (AI) can address issues related to complex diseases, aging populations, and workforce shortages. AI solutions can streamline patient flow, improve care coordination, and support personalized healthcare management. By assessing the impact of AI on patient care, healthcare efficiency, and effectiveness can be enhanced. This research highlights the challenges and opportunities in CDSS adoption in developing countries, with implications for healthcare improvement in Saudi Arabia.

Technology readiness assessment: Case of clinical decision support systems in healthcare

Clinical Decision Support Systems (CDSS) play a critical role in modern healthcare by supporting healthcare providers in making well-informed decisions, improving patient safety and outcomes, enhancing efficiency, and promoting evidence-based practices. Their integration into clinical workflows can lead to more effective and patient-centered care. CDSS is essential tools for healthcare organizations as well as for healthcare providers to improve clinical care. However, successful implementation of CDSS can be challenging. Therefore, before implementing CDSS, it is crucial to assess the readiness of healthcare organizations to implement these tools. CDSS is essential tools in healthcare for several compelling reasons. For instance, enhanced patient safety, improved diagnostic accuracy, optimized treatment plans, consistency in care, and support for complex decisions. This study's aim is to develop a model that will help healthcare organizations identify the challenges of implementing CDSS, and to assess their readiness for such an implementation in a comprehensive and multi-dimensional manner. Through a literature review, the first step of this research explores the concept of clinical decision support and CDSS, discussing their features, characteristics, and organizational hurdles to implementation. It also provides perspectives on CDSS adoption in the context of information systems and health technology. The review helped to identify research gaps, objectives, and questions. To address these gaps and to attempt to answer the research questions, a Hierarchical Decision Model (HDM) is proposed. The model allows us to assess the readiness of healthcare organizations for CDSS implementation. It presents four perspectives and sixteen criteria for a multi-dimensional assessment. The methodology involves expert panels for the HDM model's refinement, validation, and quantification. Two case studies are then presented to demonstrate the HDM model's application to identify real-world CDSS implementation challenges and to provide insights and recommendations. The research contributions are evaluated against the identified gaps in the literature review, with limitations and future research presented. In conclusion, this research provides valuable insights into CDSS implementation readiness assessment and highlights the need for careful consideration and planning. The proposed HDM model offers a valuable framework for healthcare organizations to evaluate their readiness for CDSS implementation.

Influential factors in the adoption of clinical decision support systems in hospital settings: a systematic review and meta-synthesis of qualitative studies

PurposeAdoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.Design/methodology/approachA systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.FindingsThirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.Practical implicationsThis study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.Originality/valueTo the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective

The concept of Explainable Artificial Intelligence (XAI) provides a clear and comprehensible explanation for the reasoning behind a system's output, allowing users to understand the context in which it operates. In the realm of Clinical Decision Support Systems (CDSS), XAI is particularly crucial, as it helps healthcare professionals (HCPs) in their decision-making processes. Without XAI, there is a risk of over-reliance on the system's output, potentially leading to subpar results. Despite the numerous benefits that XAI-enhanced CDSS hold in the healthcare industry, there has been a limited number of studies examining their implementation and acceptance. Thus, the objective of this study was to examine the adoption of XAI-based CDSS through the Stimulus-Organism-Response model. The sample consisted of 172 HCPs from Malaysian public and private hospitals, and the research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) in conjunction with the bootstrapping method. The results showed a significant positive correlation between the stimulus factors of informed action, transparent interaction, and representational fidelity and the positive attitude towards XAI-based CDSS as an organism factor. Additionally, the study found that attitude was a significant predictor of the intention to adopt as a response factor. The analysis also revealed a negative and significant moderation effect of perceived performance risk on the relationship between attitude and intention, while the positive moderating effect of perceived fairness was not supported. The findings of this study have significant implications for both theoretical and practical considerations and highlight the importance of XAI in the field of Clinical Decision Support Systems.

Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review

IntroductionArtificial intelligence (AI) technologies are increasingly applied to empower clinical decision support systems (CDSS), providing patient-specific recommendations to improve clinical work. Equally important to technical advancement is human, social, and contextual factors that impact the successful implementation and user adoption of AI-empowered CDSS (AI-CDSS). With the growing interest in human-centered design and evaluation of such tools, it is critical to synthesize the knowledge and experiences reported in prior work and shed light on future work.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review to gain an in-depth understanding of how AI-empowered CDSS was used, designed, and evaluated, and how clinician users perceived such systems. We performed literature search in five databases for articles published between the years 2011 and 2022. A total of 19874 articles were retrieved and screened, with 20 articles included for in-depth analysis.ResultsThe reviewed studies assessed different aspects of AI-CDSS, including effectiveness (e.g., improved patient evaluation and work efficiency), user needs (e.g., informational and technological needs), user experience (e.g., satisfaction, trust, usability, workload, and understandability), and other dimensions (e.g., the impact of AI-CDSS on workflow and patient-provider relationship). Despite the promising nature of AI-CDSS, our findings highlighted six major challenges of implementing such systems, including technical limitation, workflow misalignment, attitudinal barriers, informational barriers, usability issues, and environmental barriers. These sociotechnical challenges prevent the effective use of AI-based CDSS interventions in clinical settings.DiscussionOur study highlights the paucity of studies examining the user needs, perceptions, and experiences of AI-CDSS. Based on the findings, we discuss design implications and future research directions.

Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.

Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption. To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS. We derived task-specific MMs using four approaches: (1) a GUI-based data quality analysis using the open source tool openCQA. (2) Analyzing cases of known false CDSS decisions. (3) Data-driven learning on MM-results. (4) A systematic check to find blind spots in our set of MMs based on the HIDQF data quality framework. We expressed the derived data quality-related knowledge about the CDSS using the 5-tuple-formalization for MMs. We identified some task-specific dataset characteristics that a targeted data quality assessment for our use case should inspect. Altogether, we defined 394 MMs organized in 13 data quality knowledge bases. We have created a set of shareable, applicable MMs that can support targeted data quality assessment for CDSS-based systemic inflammatory response syndrome (SIRS) detection in critically ill, pediatric patients. With the demonstrated approaches for deriving and expressing task-specific MMs, we intend to help promoting targeted data quality assessment as a commonly recognized usual part of research on data-consuming application systems in health care.

OP196 Clinical Decision Support Systems (CDSS) For Antibiotic Management: Factors Limiting Sustainable Digital Transformation

IntroductionClinical decision support systems (CDSS) are being developed to support evidence-based antibiotic prescribing and reduce the risk of inappropriate or over-prescribing; however, adoption of CDSS into the health system is rarely sustained. We aimed to understand the implementation challenges at a macro (policymakers), meso (organizational) and micro-level (individual practices) to identify the drivers of CDSS non-adoption.MethodsWe have adopted a mixed-method study design which comprised of: (i) systematic review and meta-analysis to assess the impact of CDSS on appropriate antibiotic prescribing, (ii) Online survey of clinicians in Australia from hospitals and primary care to identify drivers of CDSS adoption and (iii) in-depth interviews with policymakers to evaluate policy-level challenges and opportunities to CDSS implementation.ResultsCDSS implementation can improve compliance with antibiotic prescribing guidelines, with a relative decrease in mortality, volume of antibiotic use and length of hospital stay. However, CDSS provision alone is not enough to achieve these benefits. Important predictors of clinicians’ perception regarding CDSS adoption include the seniority of clinical end-users (years), use of CDSS, and the care setting. Clinicians in primary care and those with significant clinical experience are less likely to use CDSS due to a lack of trust in the system, fear of comprising professional autonomy, and patients’ expectations. Lack of important policy considerations for CDSS integration into a multi-stakeholder healthcare system has limited the organizational capacity to foster change and align processes to support the innovation.ConclusionsThese results using multiple lines of evidence highlight the importance of a holistic approach when undertaking health technology management. There needs to be system-wide guidance that integrates individual, organizational and system-level factors when implementing CDSS so that effective antibiotic stewardship can be facilitated.

Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Intelligence amplification exploits the opportunities of artificial intelligence, which includes data analytic techniques and codified knowledge for increasing the intelligence of human decision makers. Intelligence amplification does not replace human decision makers but may help especially professionals in making complex decisions by well-designed human-AI system learning interactions (i.e., triple loop learning). To understand the adoption challenges of intelligence amplification systems, we analyse the adoption of clinical decision support systems (CDSS) as an organizational learning process by the case of a CDSS implementation for deciding on administering antibiotics to prematurely born babies. We identify user-oriented single and double loop learning processes, triple loop learning, and institutional deutero learning processes as organizational learning processes that must be realized for effective intelligence amplification adoption. We summarize these insights in a system dynamic model—containing knowledge stocks and their transformation processes—by which we analytically structure insights from the diverse studies of CDSS and intelligence amplification adoption and by which intelligence amplification projects are given an analytic theory for their design and management. From our case study, we find multiple challenges of deutero learning that influence the effectiveness of IA implementation learning as transforming tacit knowledge into explicit knowledge and explicit knowledge back to tacit knowledge. In a discussion of implications, we generate further research directions and discuss the generalization of our case findings to different organizations.

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.

Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.

The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health record data to assist nurses and physicians in prognostic and treatment decision making (ie, predictive CDSSs) in the hospital. A systematic search of PubMed, CINAHL, and IEEE Xplore and hand-searching of relevant conference proceedings were conducted to identify eligible articles. Empirical studies of predictive CDSSs using electronic health record data for nurses or physicians in the hospital setting published in the last 5 years in peer-reviewed journals or conference proceedings were eligible for synthesis. Data from eligible studies regarding clinician involvement, stage in system design, predictive CDSS intention, and target clinician were charted and summarized. Eighty studies met eligibility criteria. Clinical expert involvement was most prevalent at the beginning and late stages of system design. Most articles (95%) described developing and evaluating machine learning models, 28% of which described involving clinical experts, with nearly half functioning to verify the clinical correctness or relevance of the model (47%). Involvement of clinical experts in predictive CDSS design should be explicitly reported in publications and evaluated for the potential to overcome predictive CDSS adoption challenges. If present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated. However, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.

The Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping Review.

Clinical Decision Support Systems (CDSSs) are used in a clinical setting to help physicians make decisions to improve clinical performance and patient care. There are many benefits to the implementation and adoption of CDSSs, such as reducing the rate of misdiagnosis, improving efficiency and patient care, and reducing the risk of medication errors. On the other hand, CDSSs can have several disadvantages. For example, physicians can see CDSSs as a threat to their clinical autonomy. CDSSs can also be very costly to adopt, maintain, and support. These advantages and disadvantages can have both positive and negative impacts on physicians. We conducted a scoping review to explore the impact of CDSSs on physicians. We searched the following electronic databases: CINAHL, PubMed, and Google Scholar. Two reviewers independently selected the retrieved studies and extracted data from the included studies. A narrative approach was used to synthesize the extracted data. We included 14 studies of the 300 retrieved studies. We identified the following positive impacts: work efficiency, providing more personalized care, improving care and knowledge, increasing confidence in making decisions, improving prescribing behavior, and reducing the number of ordered laboratory and medical imaging tests. Several negative impacts were also reported by the studies, namely: inefficient documentation, interruption in the patient-physician communication, and an increase in unnecessary referrals.

The Role of Decision Support Systems in Attenuating Racial Biases in Healthcare Delivery

Although significant research has examined how technology can intensify racial and other outgroup biases, limited work has investigated the role information systems can play in abating them. Racial biases are particularly worrisome in healthcare, where underrepresented minorities suffer disparities in access to care, quality of care, and clinical outcomes. In this paper, we examine the role clinical decision support systems (CDSS) play in attenuating systematic biases among black patients, relative to white patients, in rates of amputation and revascularization stemming from diabetes mellitus. Using a panel of inpatient data and a difference-in-difference approach, results suggest that CDSS adoption significantly shrinks disparities in amputation rates across white and black patients—with no evidence that this change is simply delaying eventual amputations. Results suggest that this effect is driven by changes in treatment care protocols that match patients to appropriate specialists, rather than altering within physician decision making. These findings highlight the role information systems and digitized patient care can play in promoting unbiased decision making by structuring and standardizing care procedures. This paper was accepted by Stefan Scholtes, healthcare management.

Machine learning applications in critical care

The use of machine learning (ML) applications in the intensive care units (ICUs) has surged over the last two decades. This is the result of the digital transformation that many health-care organizations have implemented. Data that are generated in the process of intensive care have more volume, velocity, and value than data generated in any other general hospital's department. This characteristic of ICUs makes them attractive environments for developing models that require rich dataset. ML has been used to develop clinical decision support system (CDSS) that could make informative decisions without requiring prior in-depth knowledge about the roots of the disease or common characteristics of the patients. The adoption of ML-based CDSS in ICUs is continuously increasing as ML algorithms achieve high levels of accuracy in descriptive, diagnostic, predictive, and prescriptive decisions. This article reviews some of the applications of ML in ICUs. This article will show examples of how ML was used for outcome predictions, such as predicting mortality and readmission. Examples in this article also include using ML for diagnostic and image recognition purposes. This review will discuss the use of ML for monitoring ICU patients, whether monitoring their physical safety with artificial intelligence vision detection algorithms, monitoring their continuous bedside measurements, or monitoring the administration and dosage of their medications. All these examples show that ML-based CDSS are on the path for a journey full of innovative and creative solutions that will increase the quality, efficiency, and effectiveness of critical care.

ANALYSIS AND REVIEW OF PRESCRIBING CLINICAL DECISION SUPPORT SYSTEM WITHIN THE CONTEXT OF NHS SECONDARY SECTOR

Clinical Decision Support System (CDSS) is an important and emerging area of research. Therefore, the study of CDSS form an important field of information technology (IT) used in health care industry. Diagnosis and decision making form an integral part of CDSS. Based on the healthcare data and patient’s medical history, CDSS performs recommendation, which helps healthcare professionals to diagnose properly and make clinical decisions accordingly. In this paper, we present analysis and review of prescribing CDSS within the context of the secondary sector of the United Kingdom (UK) National Health Service (NHS). It was observed that prescribing CDSS enhances the safety of patients by reducing medication errors that occur during traditional prescribing (non-electronic prescribing). The reduction of medical errors is due to the efficiency of prescribing CDSS in facilitating health care providers with a holistic view of patient health records and any drug allergy or drug-drug interactions. This study showed that successful adoption of CDSS within the NHS secondary sector faces challenges such as physician acceptance, training, and interoperability. Article DOI: https://dx.doi.org/10.20319/lijhls.2018.43.6071 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

A fuzzy framework for encoding uncertainty in clinical decision-making

In recent decades, technological advances coupled with research efforts have made possible to develop very complex Decision Support Systems (DSSs) able to exhibit highly sophisticated reasoning capabilities in order to improve clinical decision-making, and, thus, promote more efficient care practices. One of the most significant factors influencing, and in particular limiting, the adoption of clinical DSSs is represented by the modality of representation and computerization of clinical guidelines in form of patient-specific recommendations. Until now, many knowledge representation formalisms have been developed, mainly focused on time-oriented guidelines. However, they can generate an unrealistic over-simplification of reality, since they are not able to completely handle uncertainty and imprecision typically affecting clinical guidelines. In this respect, this paper proposes a novel fuzzy framework expressly thought for building guideline-based DSSs, by efficiently modelling and handling the peculiarities of clinical knowledge affected by uncertainty and imprecision and encoded in the form of guidelines. This framework has been devised with the aim of: (i) offering a set of patterns for easily inserting and editing clinical recommendations belonging to a guideline as a group of one or more fuzzy rules expressing positive evidence and one fuzzy ELSE rule including negative evidence; (ii) defining a set of Fuzzy Guideline Systems (FGSs), one for each guideline encoded, characterized by ad-hoc configurations for the mathematical operators necessary to evaluate rules and generate the outcome expected; (iii) implementing a multi-level inference scheme able to treat different FGSs as a whole and efficiently enable their interconnection, i.e. the chaining among the groups of fuzzy rules belonging to each FGS; (iv) exposing a set of graphical facilities for guiding the definition of fuzzy rules to be embedded into a clinical DSS and enabling their automatic encoding and execution by using an XML-based machine executable language. A usability evaluation has been performed, showing a good satisfaction of medical users with respect to the framework implemented, and, thus, proving both its feasibility and usefulness.

Clinical Decision Support using a Terminology Server to improve Patient Safety.

Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation. This paper details the deployment of a CDSS jointly with the deployment of a Terminology Server (TS) within the AHPS infrastructure. It also explains a case study about the application of decision support to thromboembolism patients and its potential impact on improving patient safety. We will apply the inSPECt tool proposal to evaluate the appropriateness of alerts in this scenario.

Differences in pneumonia treatment between high-minority and low-minority neighborhoods with clinical decision support system implementation

Background: The relationship between clinical decision support systems (CDSS) and quality is a relatively new, and in light of the new health information technology (HIT) legislation, policy-relevant area. Moreover, very few studies exist examining the link between HIT and healthcare disparities. The purpose of this article is to examine the association between CDSS and the treatment of pneumonia care within high-minority (≥29.1% non-White, non-Hispanic) and low-minority (<29.1%) Zip Code Tabulation Areas (ZCTAs). Research design: This study employed a cross-sectional design and used 2009 data from the American Hospital Association, the Centers for Medicare and Medicaid Services and the Research Triangle Institute. Adjusted analysis controlled for a hospital’s propensity to use CDSS. Results: In the unadjusted analysis, hospitals in high-minority ZCTAs had lower pneumonia quality composite scores than their low-minority counterparts. When adjusting for other hospital and ZCTA-level variables, we found that CDSS use had stronger positive associations with quality in high-minority hospitals. Conclusions: Results support policy directives may support higher quality improvements by focusing CDSS adoption in high-minority hospitals.

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

The objective of the study was to identify potential barriers and facilitators to improve clinical practice using computer-based Clinical Decision Support System (CDSS). Studies published since 2000 were found using PubMed database, PsychInfo, CINAHL, EBSCO host database, and Google scholar. Twenty-six relevant publications were examined. Thirty-five unique barriers and twenty-five unique facilitators were identified in the literature as important determinants of CDSS’s adoption in clinical practice. The list of barriers and facilitators collected from each study were then organized under the four dimensions of The Unified Theory of Acceptance and Use of Technology (UTAUT) model: performance expectancy, effort expectancy, social influence, and facilitating conditions. Some of the important barriers to CDSS use include; lack of time or time constraints, economic constraints (e.g., finance and resources), lack of knowledge of system or content, reluctance to use system in front of patients, obscure workflow issues, less authenticity or reliability of information, lack of agreement with the system, and physician or user attitude toward the system. The study contributes immensely to the literature by identifying the important barriers and facilitators of CDSS.

Expanding usability analysis with intrinsic motivation concepts to learn about CDSS adoption: a case study

ObjectivesDespite many clinical decision support systems (CDSSs) being rated as highly usable, CDSSs have not been widely adopted in clinical practice. We posit that there are factors aside from usability that impact adoption of CDSSs; in particular we are interested in the role played by MDs intrinsic motivation to use computer-based support. Our research aim is to investigate the relationship between usability and intrinsic motivation in order to learn about adoption of CDSS in clinical practice. MethodsFollowing the evaluation of a CDSS, 19 MDs completed a 2 part questionnaire about their intrinsic motivation to use computer-based support in general and the usability of the evaluated CDSS. ResultsThe analysis of MDs motivation to use computer-based support demonstrated that MDs are comfortable using computer-based support and in general find using it quite easy (a motivation rating of 0.66 on a (0, 1) scale was computed). However MDs also reported a perceived lack of competence associated with a lack of prior experience using technology in practice, which results in pressure and tension. The considered CDSS scored highly on all usability dimensions and a usability rating of 0.74 was recorded. The examination of the relationship between motivation and usability suggested that users who were motivated to use computer-based support experienced better usability than those who reported low levels of motivation. ConclusionsOur small case study suggests that an important factor supplementing the usability of CDSSs is intrinsic motivation to use computer-based support in general. We posit that the lack of such a measure thus far in CDSS evaluation may to some extent explain seeming MD satisfaction with CDSSs on one hand, but their limited adoption on the other. We recommend that clinical managers responsible for deploying CDSS should invest in training MDs to use technology underlying computer-based support applications instead of focusing only on the features of the specific CDSS to be deployed.

Management Issues in Healthcare Information Technology

Management Issues in Healthcare Information Technology