Clinical decision support capabilities of commercially-available clinical information systems

Reusable Generic Clinical Decision Support System Module for Immunization Recommendations in Resource-Constraint Settings

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules. Our solution includes a common CDSS module, electronic medical record (EMR) specific adapters, CDSS rules written in the clinical quality language (CQL) (derived from CDC immunization recommendations), and patient records in fast healthcare interoperability resources (FHIR) format. The proposed CDSS is entirely browser-based and reachable within the user's EMR interface at the client-side. This helps to avoid the transmission of patient data and privacy breaches. Additionally, we propose to provide means of managing and maintaining CDSS rules to allow the end users to modify them independently. Successful implementation and deployment were achieved in OpenMRS and OpenEMR during initial testing.

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design

BACKGROUND

Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills.

OBJECTIVE

In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow.

METHODS

First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs.

RESULTS

We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure.

CONCLUSIONS

The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

Clinical decision support systems in community pharmacies: a scoping review

OBJECTIVE

Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.

MATERIALS AND METHODS

Six databases were searched using a combination of controlled vocabulary and keywords relating to community pharmacy and CDSS. After deduplicating the initial search results, 2 independent reviewers conducted title/abstract screening and full-text review. Then, the selected studies were synthesized in terms of investigational/clinical focuses.

RESULTS

The selected 21 studies investigated the perception of and response to CDSS alerts (n = 7), the impact of CDSS alerts (n = 7), and drug-drug interaction (DDI) alerts (n = 8). Three causes of the failures to prevent DDIs of clinical importance have been noted: the perception of and response to a high volume of DDI alerts, a suboptimal performance of CDSS, and a dearth of sociotechnical considerations for managing workload and workflow. Additionally, 7 studies emphasized the importance of utilizing CDSS for a specific clinical focus, ie, antibiotics, diabetes, opioids, and vaccinations.

CONCLUSION

Despite the range of topics dealt in the last 30 years, this scoping review confirms that research on CDSS in community pharmacies is limited and disjointed, lacking a comprehensive approach to highlight areas for improvement and ways to optimize CDSS utilization.

Computer-Assisted Clinical Diagnosis and Treatment

PURPOSE OF REVIEW

Computer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes.

RECENT FINDINGS

CAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.

A lifecycle framework illustrates eight stages necessary for realizing the benefits of patient-centered clinical decision support

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following stages: Computable Clinical Knowledge, Patient-specific Inference, Information Delivery, Clinical Decision, Patient Behaviors, Health Outcomes, Aggregate Data, and patient-centered outcomes research (PCOR) Evidence. Using this idealized framework reminds key stakeholders that developing, deploying, and evaluating PC-CDS is a complex, sociotechnical challenge that requires consideration of all 8 stages. In addition, we need to ensure that patients, their caregivers, and the clinicians caring for them are explicitly involved at each stage to help us achieve the quintuple aim.

The Reasons for Physicians and Pharmacists' Acceptance of Clinical Support Systems in Saudi Arabia

This research aims to identify the technological and non-technological factors influencing user acceptance of the CDSS in a group of healthcare facilities in Saudi Arabia. The study proposes an integrated model that indicates the factors to be considered when designing and evaluating CDSS. This model is developed by integrating factors from the "Fit between Individuals, Task, and Technology" (FITT) framework into the three domains of the human, organization, and technology-fit (HOT-fit) model. The resulting FITT-HOT-fit integrated model was tested using a quantitative approach to evaluate the currently implemented CDSS as a part of Hospital Information System BESTCare 2.0 in the Saudi Ministry of National Guard Health Affairs. For data collection, a survey questionnaire was conducted at all Ministry of National Guard Health Affairs hospitals. Then, the collected survey data were analyzed using Structural Equation Modeling (SEM). This analysis included measurement instrument reliability, discriminant validity, convergent validity, and hypothesis testing. Moreover, a CDSS usage data sample was extracted from the data warehouse to be analyzed as an additional data source. The results of the hypotheses test show that usability, availability, and medical history accessibility are critical factors influencing user acceptance of CDSS. This study provides prudence about healthcare facilities and their higher management to adopt CDSS.

Conceptualizing clinical decision support as complex interventions: a meta-analysis of comparative effectiveness trials

OBJECTIVES

Complex interventions with multiple components and behavior change strategies are increasingly implemented as a form of clinical decision support (CDS) using native electronic health record functionality. Objectives of this study were, therefore, to (1) identify the proportion of randomized controlled trials with CDS interventions that were complex, (2) describe common gaps in the reporting of complexity in CDS research, and (3) determine the impact of increased complexity on CDS effectiveness.

MATERIALS AND METHODS

To assess CDS complexity and identify reporting gaps for characterizing CDS interventions, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting tool for complex interventions. We evaluated the effect of increased complexity using random-effects meta-analysis.

RESULTS

Most included studies evaluated a complex CDS intervention (76%). No studies described use of analytical frameworks or causal pathways. Two studies discussed use of theory but only one fully described the rationale and put it in context of a behavior change. A small but positive effect (standardized mean difference, 0.147; 95% CI, 0.039-0.255; P < .01) in favor of increasing intervention complexity was observed.

DISCUSSION

While most CDS studies should classify interventions as complex, opportunities persist for documenting and providing resources in a manner that would enable CDS interventions to be replicated and adapted. Unless reporting of the design, implementation, and evaluation of CDS interventions improves, only slight benefits can be expected.

CONCLUSION

Conceptualizing CDS as complex interventions may help convey the careful attention that is needed to ensure these interventions are contextually and theoretically informed.

Clinician collaboration to improve clinical decision support: the Clickbusters initiative

OBJECTIVE

We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts.

MATERIALS AND METHODS

We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program.

RESULTS

Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS.

CONCLUSIONS

At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

Fallacy of Median Door‐to‐ECG Time: Hidden Opportunities for STEMI Screening Improvement

Background

ST‐segment elevation myocardial infarction (STEMI) guidelines recommend screening arriving emergency department (ED) patients for an early ECG in those with symptoms concerning for myocardial ischemia. Process measures target median door‐to‐ECG (D2E) time of 10 minutes.

Methods and Results

This 3‐year descriptive retrospective cohort study, including 676 ED‐diagnosed patients with STEMI from 10 geographically diverse facilities across the United States, examines an alternative approach to quantifying performance: proportion of patients meeting the goal of D2E≤10 minutes. We also identified characteristics associated with D2E>10 minutes and estimated the proportion of patients with screening ECG occurring during intake, triage, and main ED care periods. We found overall median D2E was 7 minutes (IQR:4–16; range: 0–1407 minutes; range of ED medians: 5–11 minutes). Proportion of patients with D2E>10 minutes was 37.9% (ED range: 21.5%–57.1%). Patients with D2E>10 minutes, compared to those with D2E≤10 minutes, were more likely female (32.8% versus 22.6%, P=0.005), Black (23.4% versus 12.4%, P=0.005), non‐English speaking (24.6% versus 19.5%, P=0.032), diabetic (40.2% versus 30.2%, P=0.010), and less frequently reported chest pain (63.3% versus 87.4%, P<0.001). ECGs were performed during ED intake in 62.1% of visits, ED triage in 25.3%, and main ED care in 12.6%.

Conclusions

Examining D2E>10 minutes can identify opportunities to improve care for more ED patients with STEMI. Our findings suggest sex, race, language, and diabetes are associated with STEMI diagnostic delays. Moving the acquisition of ECGs completed during triage to intake could achieve the D2E≤10 minutes goal for 87.4% of ED patients with STEMI. Sophisticated screening, accounting for differential risk and diversity in STEMI presentations, may further improve timely detection.

Indication alerts to improve problem list documentation

BACKGROUND

Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication.

METHODS

We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review.

RESULTS

Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%.

CONCLUSIONS

Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

Translating an evidence-based clinical pathway into shareable CDS: developing a systematic process using publicly available tools

OBJECTIVE

To develop a process for translating semi-structured clinical decision support (CDS) into shareable, computer-readable CDS.

MATERIALS AND METHODS

We developed a systematic and transparent process using publicly available tools (eGLIA, GEM Cutter, VSAC, and the CDS Authoring Tool) to translate an evidence-based clinical pathway (CP) into a Clinical Quality Language (CQL)-encoded CDS artifact.

RESULTS

We produced a 4-phase process for translating a CP into a CQL-based CDS artifact. CP content was extracted using GEM into discrete clinical concepts, encoded using standard terminologies into value sets on VSAC, evaluated against workflows using a wireframe, and finally structured as a computer readable CDS artifact using CQL. This process included a quality control step and intermediate products to support transparency and reuse by other CDS developers.

DISCUSSION

Translating a CP into a shareable, computer-readable CDS artifact was accomplished through a systematic process. Our process identified areas of ambiguity and gaps in the CP, which generated improvements in the CP. Collaboration with clinical subject experts and the CP development team was essential for translation. Publicly available tools were sufficient to support most translation steps, but expression of certain complex concepts required manual encoding.

CONCLUSION

Standardized development of CDS from a CP is feasible using a systematic 4-phase process. CPs represent a potential reservoir for developers of evidence-based CDS. Aspects of CP development simplified portions of the CDS translation process. Publicly available tools can facilitate CDS development; however, enhanced tool features are needed to model complex CDS statements.

How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review and meta-analysis to assess: 1) changes in medication error rates and associated patient harm following electronic medication system (EMS) implementation; and 2) evidence of system-related medication errors facilitated by the use of an EMS.

MATERIALS AND METHODS

We searched Medline, Scopus, Embase, and CINAHL for studies published between January 2005 and March 2019, comparing medication errors rates with or without assessments of related harm (actual or potential) before and after EMS implementation. EMS was defined as a computer-based system enabling the prescribing, supply, and/or administration of medicines. Study quality was assessed.

RESULTS

There was substantial heterogeneity in outcomes of the 18 included studies. Only 2 were strong quality. Meta-analysis of 5 studies reporting change in actual harm post-EMS showed no reduced risk (RR: 1.22, 95% CI: 0.18-8.38, P = .8) and meta-analysis of 3 studies reporting change in administration errors found a significant reduction in error rates (RR: 0.77, 95% CI: 0.72-0.83, P = .004). Of 10 studies of prescribing error rates, 9 reported a reduction but variable denominators precluded meta-analysis. Twelve studies provided specific examples of system-related medication errors; 5 quantified their occurrence.

DISCUSSION AND CONCLUSION

Despite the wide-scale adoption of EMS in hospitals around the world, the quality of evidence about their effectiveness in medication error and associated harm reduction is variable. Some confidence can be placed in the ability of systems to reduce prescribing error rates. However, much is still unknown about mechanisms which may be most effective in improving medication safety and design features which facilitate new error risks.

Effects of combined decision-support and performance-based incentives on reported client satisfaction with maternal health services in primary facilities: A quasi-experimental study in the Upper East Region of Ghana

BACKGROUND

Computerized decision-support systems (CDSS) and performance-based incentives (PBIs) have potential to contribute to client satisfaction with health services. However, rigorous evidence is lacking on the effectiveness of these strategies in lower-income countries such as Ghana. This study aimed to determine the effect of a combined CDSS-PBI intervention on client satisfaction with maternal health services in primary facilities in the Upper East Region of Ghana.

METHODS

We employed a quasi-experimental controlled baseline and endline design to assess the effect of the combined interventions on client satisfaction with maternal health services, measured by quantitative pre/post-test client satisfaction survey. Our analysis used difference-in-difference logistic regression, controlling for potential covariates, to compare variables across intervention and comparison facilities at baseline and endline.

RESULTS

The combined CDSS-PBI intervention was associated with increased or unchanged client satisfaction with all maternal health services compared at endline. Antenatal client difference-in-difference of mean satisfaction scores were significant at endline for intervention (n = 378) and comparison (n = 362) healthcare facilities for overall satisfaction (DiD 0.058, p = 0.014), perception of providers' technical performance (DiD = 0.142; p = 0.006), client-provider interaction (DiD = 0.152; p = 0.001), and provider availability (DiD = 0.173; p = 0.001). Delivery client difference-in-difference of satisfaction scores were significant at endline for intervention (n = 318) and comparison (n = 240) healthcare facilities for overall satisfaction with delivery services (DiD = 0.072; p = 0.02) and client-provider interaction (DiD = 0.146; p = 0.02). However, mean overall satisfaction actually reduced slightly in intervention facilities, while DiD for technical performance and provider availability were not significant.

CONCLUSION

This combined CDSS-PBI intervention was associated with greater antenatal and delivery client satisfaction with some aspects of maternity services within two years of implementation. It could be expanded elsewhere if funds allow, though further research is still required to assess cost-effectiveness and long-term effects on client satisfaction and maternal health outcomes.

Development and dissemination of clinical decision support across institutions: standardization and sharing of refugee health screening modules

OBJECTIVES

We developed and piloted a process for sharing guideline-based clinical decision support (CDS) across institutions, using health screening of newly arrived refugees as a case example.

MATERIALS AND METHODS

We developed CDS to support care of newly arrived refugees through a systematic process including a needs assessment, a 2-phase cognitive task analysis, structured preimplementation testing, local implementation, and staged dissemination. We sought consensus from prospective users on CDS scope, applicable content, basic supported workflows, and final structure. We documented processes and developed sharable artifacts from each phase of development. We publically shared CDS artifacts through online dissemination platforms. We collected feedback and implementation data from implementation sites.

RESULTS

Responses from 19 organizations demonstrated a need for improved CDS for newly arrived refugee patients. A guided multicenter workflow analysis identified 2 main workflows used by organizations that would need to be supported by shared CDS. We developed CDS through an iterative design process, which was successfully disseminated to other sites using online dissemination repositories. Implementation sites had a small-to-modest analyst time commitment but reported a good match between CDS and workflow.

CONCLUSION

Sharing of CDS requires overcoming technical and workflow barriers. We used a guided multicenter workflow analysis and online dissemination repositories to create flexible CDS that has been adapted at 3 sites. Organizations looking to develop sharable CDS should consider evaluating the workflows of multiple institutions and collecting feedback on scope, design, and content in order to make a more generalizable product.

Development and Implementation of a Decision Support System to Improve Control of Hypertension and Diabetes in a Resource-Constrained Area in Brazil: Mixed Methods Study

BACKGROUND

The low levels of control of hypertension and diabetes mellitus are a challenge that requires innovative strategies to surpass barriers of low sources, distance, and quality of health care.

OBJECTIVE

The aim of this study is to develop a clinical decision support system (CDSS) for diabetes and hypertension management in primary care, to implement it in a resource-constrained region, and to evaluate its usability and health care practitioner satisfaction.

METHODS

This mixed methods study is a substudy of HealthRise Brazil Project, a multinational study designed to implement pilot programs to improve screening, diagnosis, management, and control of hypertension and diabetes among underserved communities. Following the identification of gaps in usual care, a team of clinicians established the software functional requirements. Recommendations from evidence-based guidelines were reviewed and organized into a decision algorithm, which bases the CDSS reminders and suggestions. Following pretesting and expert panel assessment, pilot testing was conducted in a quasi-experimental study, which included 34 primary care units of 10 municipalities in a resource-constrained area in Brazil. A Likert-scale questionnaire evaluating perceived feasibility, usability, and utility of the application and professionals' satisfaction was applied after 6 months. In the end-line assessment, 2 focus groups with primary care physicians and nurses were performed.

RESULTS

A total of 159 reminders and suggestions were created and implemented for the CDSS. At the 6-month assessment, there were 1939 patients registered in the application database and 2160 consultations were performed by primary care teams. Of the 96 health care professionals who were invited for the usability assessment, 26% (25/96) were physicians, 46% (44/96) were nurses, and 28% (27/96) were other health professionals. The questionnaire included 24 items on impressions of feasibility, usability, utility, and satisfaction, and presented global Cronbach α of .93. As for feasibility, all professionals agreed (median scores of 4 or 5) that the application could be used in primary care settings and it could be easily incorporated in work routines, but physicians claimed that the application might have caused significant delays in daily routines. As for usability, overall evaluation was good and it was claimed that the application was easy to understand and use. All professionals agreed that the application was useful (score 4 or 5) to promote prevention, assist treatment, and might improve patient care, and they were overall satisfied with the application (median scores between 4 and 5). In the end-line assessment, there were 4211 patients (94.82% [3993/4211] with hypertension and 24.41% [1028/4211] with diabetes) registered in the application's database and 7960 consultations were performed by primary health care teams. The 17 participants of the focus groups were consistent to affirm they were very satisfied with the CDSS.

CONCLUSIONS

The CDSS was applicable in the context of primary health care settings in low-income regions, with good user satisfaction and potential to improve adherence to evidence-based practices.

Clinical Decision Support for Worker Health: A Five-Site Qualitative Needs Assessment in Primary Care Settings

BACKGROUND

Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices.

OBJECTIVE

This study aimed to identify the need for, and barriers and facilitators related to, implementation of CDS tools for the clinical management of working patients in a variety of primary care settings.

METHODS

We used a qualitative design that included analysis of interview transcripts and observational field notes from 10 clinics in five organizations.

RESULTS

We interviewed 83 providers, staff members, managers, informatics and information technology experts, and leaders and spent 35 hours observing. We identified eight themes in four categories related to CDS for worker health (operational issues, usefulness of proposed CDS, effort and time-related issues, and topic-specific issues). These categories were classified as facilitators or barriers to the use of the CDS tools. Facilitators related to operational issues include current technical feasibility and new work patterns associated with the coordinated care model. Facilitators concerning usefulness include users' need for awareness and evidence-based tools, appropriateness of the proposed CDS for their patients, and the benefits of population health data. Barriers that are effort-related include additional time this proposed CDS might take, and other pressing organizational priorities. Barriers that are topic-specific include sensitive issues related to health and work and the complexities of information about work.

CONCLUSION

We discovered several themes not previously described that can guide future CDS development: technical feasibility of the proposed CDS within commercial EHRs, the sensitive nature of some CDS content, and the need to assist the entire health care team in managing worker health.

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

BACKGROUND

Drug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients.

METHODS

For the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The "Questionnaire for User Interface Satisfaction" was used to measure user satisfaction of the human-computer interface.

RESULTS

Among 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system.

CONCLUSIONS

To our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

Health information technology:Fallacies and Sober realities - Redux A homage to Bentzi Karsh and Robert Wears

Since the publication of "Health Information Technology: Fallacies and Sober Realities" in 2010, health information technology (HIT) has become nearly ubiquitous in US healthcare facilities. Yet, HIT has yet to achieve its putative benefits of higher quality, safer, and lower cost care. There has been variable but largely marginal progress at addressing the 12 HIT fallacies delineated in the original paper. Here, we revisit several of the original fallacies and add five new ones. These fallacies must be understood and addressed by all stakeholders for HIT to be a positive force in achieving the high value healthcare system the nation deserves. Foundational cognitive and human factors engineering research and development continue to be essential to HIT development, deployment, and use.

Clinical Decision Support for Early Recognition of Sepsis

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support

Genome-enabled or molecular clinical decision support (CDS) systems provide unique advantages for the clinical use of genomic data; however, their implementation is complicated by technical, biological, and systemic barriers. This article reviews the substantial technical progress that has been made in the past decade and finds that the underlying biological limitations of genomics as well as systemic barriers to adoption of molecular CDS have been comparatively underestimated. A hybrid consultative CDS system, which integrates a genomics consultant into an active CDS system, may provide an interim path forward.

A patient-oriented clinical decision support system for CRC risk assessment and preventative care

Background

Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.

Methods

We implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences.

Results

A prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty).

Conclusions

This CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0691-x) contains supplementary material, which is available to authorized users.

Wearables and the medical revolution

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Data-driven approach to Early Warning Score-based alert management

Background

Increasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals.

Methods

We quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure.

Results

We compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics.

Conclusion

EWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.

Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease

BACKGROUND

Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes.

OBJECTIVES

We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs.

METHODS

The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems.

RESULTS

The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance.

CONCLUSIONS

Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.

Thinking Together: Modeling Clinical Decision-Support as a Sociotechnical System

Computerized clinical decision-support systems are members of larger sociotechnical systems, composed of human and automated actors, who send, receive, and manipulate artifacts. Sociotechnical consideration is rare in the literature. This makes it difficult to comparatively evaluate the success of CDS implementations, and it may also indicate that sociotechnical context receives inadequate consideration in practice. To facilitate sociotechnical consideration, we developed the Thinking Together model, a flexible diagrammatical means of representing CDS systems as sociotechnical systems. To develop this model, we examined the literature with the lens of Distributed Cognition (DCog) theory. We then present two case studies of vastly different CDSSs, one almost fully automated and the other with minimal automation, to illustrate the flexibility of the Thinking Together model. We show that this model, informed by DCog and the CDS literature, are capable of supporting both research, by enabling comparative evaluation, and practice, by facilitating explicit sociotechnical planning and communication.

Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis

Objective

To conduct a systematic review and meta-analysis of the impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay (LOS), and mortality in intensive care units (ICUs).

Methods

We searched for English-language literature published between January 2000 and January 2016 using Medline, Embase, and CINAHL. Titles and abstracts of 586 unique citations were screened. Studies were included if they: (1) reported results for an ICU population; (2) evaluated the impact of CPOE or the addition of CDSSs to an existing CPOE system; (3) reported quantitative data on medication errors, ICU LOS, hospital LOS, ICU mortality, and/or hospital mortality; and (4) used a randomized controlled trial or quasi-experimental study design.

Results

Twenty studies met our inclusion criteria. The transition from paper-based ordering to commercial CPOE systems in ICUs was associated with an 85% reduction in medication prescribing error rates and a 12% reduction in ICU mortality rates. Overall meta-analyses of LOS and hospital mortality did not demonstrate a significant change.

Discussion and Conclusion

Critical care settings, both adult and pediatric, involve unique complexities, making them vulnerable to medication errors and adverse patient outcomes. The currently limited evidence base requires research that has sufficient statistical power to identify the true effect of CPOE implementation. There is also a critical need to understand the nature of errors arising post-CPOE and how the addition of CDSSs can be used to provide greater benefit to delivering safe and effective patient care.

Clinical decisions support malfunctions in a commercial electronic health record

OBJECTIVES

Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection.

METHODS

We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated.

RESULTS

Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44.

DISCUSSION

CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively.

CONCLUSION

CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Development and use of a clinical decision support tool for behavioral health screening in primary care clinics

OBJECTIVE

Screening, brief intervention, and referral for treatment (SBIRT) for behavioral health (BH) is a key clinical process. SBIRT tools in electronic health records (EHR) are infrequent and rarely studied. Our goals were 1) to design and implement SBIRT using clinical decision support (CDS) in a commercial EHR; and 2) to conduct a pragmatic evaluation of the impact of the tools on clinical outcomes.

METHODS

A multidisciplinary team designed SBIRT workflows and CDS tools. We analyzed the outcomes using a retrospective descriptive convenience cohort with age-matched comparison group. Data extracted from the EHR were evaluated using descriptive statistics.

RESULTS

There were 2 outcomes studied: 1) development and use of new BH screening tools and workflows; and 2) the results of use of those tools by a convenience sample of 866 encounters. The EHR tools developed included a flowsheet for documenting screens for 3 domains (depression, alcohol use, and prescription misuse); and 5 alerts with clinical recommendations based on screening; and reminders for annual screening. Positive screen rate was 21% (≥1 domain) with 60% of those positive for depression. Screening was rarely positive in 2 domains (11%), and never positive in 3 domains. Positive and negative screens led to higher rates of documentation of brief intervention (BI) compared with a matched sample who did not receive screening, including changes in psychotropic medications, updated BH terms on the problem list, or referral for BH intervention. Clinical process outcomes changed even when screening was negative.

CONCLUSIONS

Modified workflows for BH screening and CDS tools with clinical recommendations can be deployed in the EHR. Using SBIRT tools changed clinical process metrics even when screening was negative, perhaps due to conversations about BH not captured in the screening flowsheet. Although there are limitations to the study, results support ongoing investigation.

Testing electronic health records in the "production" environment: an essential step in the journey to a safe and effective health care system

Thorough and ongoing testing of electronic health records (EHRs) is key to ensuring their safety and effectiveness. Many health care organizations limit testing to test environments separate from, and often different than, the production environment used by clinicians. Because EHRs are complex hardware and software systems that often interact with other hardware and software systems, no test environment can exactly mimic how the production environment will behave. An effective testing process must integrate safely conducted testing in the production environment itself, using test patients. We propose recommendations for how to safely incorporate testing in production into current EHR testing practices, with suggestions regarding the incremental release of upgrades, test patients, tester accounts, downstream personnel, and reporting.

A Multidisciplinary Sepsis Program Enabled by a Two-Stage Clinical Decision Support System: Factors That Influence Patient Outcomes

Sepsis is an inflammatory response triggered by infection, with risk of in-hospital mortality fueled by disease progression. Early recognition and intervention by multidisciplinary sepsis programs may reverse the inflammatory response among at-risk patient populations, potentially improving outcomes. This retrospective study of a sepsis program enabled by a 2-stage sepsis Clinical Decision Support (CDS) system sought to evaluate the program's impact, identify early indicators that may influence outcomes, and uncover opportunities for quality improvement. Data encompassed 16 527 adult hospitalizations from 2014 and 2015. Of 2108 non-intensive care unit patients screened-in by sepsis CDS, 97% patients were stratified by 177 providers. Risk of adverse outcome improved 30% from baseline to year end, with gains materializing and stabilizing at month 7 after sepsis program go-live. Early indicators likely to influence outcomes include patient age, recent hospitalization, electrolyte abnormalities, hypovolemic shock, hypoxemia, patient location when sepsis CDS activated, and specific alert patterns.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Trends in biomedical informatics: automated topic analysis of JAMIA articles

Biomedical Informatics is a growing interdisciplinary field in which research topics and citation trends have been evolving rapidly in recent years. To analyze these data in a fast, reproducible manner, automation of certain processes is needed. JAMIA is a "generalist" journal for biomedical informatics. Its articles reflect the wide range of topics in informatics. In this study, we retrieved Medical Subject Headings (MeSH) terms and citations of JAMIA articles published between 2009 and 2014. We use tensors (i.e., multidimensional arrays) to represent the interaction among topics, time and citations, and applied tensor decomposition to automate the analysis. The trends represented by tensors were then carefully interpreted and the results were compared with previous findings based on manual topic analysis. A list of most cited JAMIA articles, their topics, and publication trends over recent years is presented. The analyses confirmed previous studies and showed that, from 2012 to 2014, the number of articles related to MeSH terms Methods, Organization & Administration, and Algorithms increased significantly both in number of publications and citations. Citation trends varied widely by topic, with Natural Language Processing having a large number of citations in particular years, and Medical Record Systems, Computerized remaining a very popular topic in all years.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax

OBJECTIVE

The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support.

METHODS AND MATERIALS

A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system.

RESULTS

We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy.

CONCLUSION

Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

Problem list completeness in electronic health records: A multi-site study and assessment of success factors

OBJECTIVE

To assess problem list completeness using an objective measure across a range of sites, and to identify success factors for problem list completeness.

METHODS

We conducted a retrospective analysis of electronic health record data and interviews at ten healthcare organizations within the United States, United Kingdom, and Argentina who use a variety of electronic health record systems: four self-developed and six commercial. At each site, we assessed the proportion of patients who have diabetes recorded on their problem list out of all patients with a hemoglobin A1c elevation>=7.0%, which is diagnostic of diabetes. We then conducted interviews with informatics leaders at the four highest performing sites to determine factors associated with success. Finally, we surveyed all the sites about common practices implemented at the top performing sites to determine whether there was an association between problem list management practices and problem list completeness.

RESULTS

Problem list completeness across the ten sites ranged from 60.2% to 99.4%, with a mean of 78.2%. Financial incentives, problem-oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture were identified as success factors at the four hospitals with problem list completeness at or near 90.0%.

DISCUSSION

Incomplete problem lists represent a global data integrity problem that could compromise quality of care and put patients at risk. There was a wide range of problem list completeness across the healthcare facilities. Nevertheless, some facilities have achieved high levels of problem list completeness, and it is important to better understand the factors that contribute to success to improve patient safety.

CONCLUSION

Problem list completeness varies substantially across healthcare facilities. In our review of EHR systems at ten healthcare facilities, we identified six success factors which may be useful for healthcare organizations seeking to improve the quality of their problem list documentation: financial incentives, problem oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture.

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study

OBJECTIVE

To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis.

DESIGN

Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside.

SETTING

Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually.

PARTICIPANTS

Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented.

MAIN OUTCOME MEASURE

'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance.

RESULTS

A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification.

CONCLUSION

A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

OBJECTIVE

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

METHODS

We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin.

RESULTS

Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules.

CONCLUSION

Significant improvements in the EHR certification and implementation procedures are necessary.

Assessing information system readiness for mitigating malpractice risk through simulation: results of a multi-site study

OBJECTIVE

To develop and test an instrument for assessing a healthcare organization's ability to mitigate malpractice risk through clinical decision support (CDS).

MATERIALS AND METHODS

Based on a previously collected malpractice data set, we identified common types of CDS and the number and cost of malpractice cases that might have been prevented through this CDS. We then designed clinical vignettes and questions that test an organization's CDS capabilities through simulation. Seven healthcare organizations completed the simulation.

RESULTS

All seven organizations successfully completed the self-assessment. The proportion of potentially preventable indemnity loss for which CDS was available ranged from 16.5% to 73.2%.

DISCUSSION

There is a wide range in organizational ability to mitigate malpractice risk through CDS, with many organizations' electronic health records only being able to prevent a small portion of malpractice events seen in a real-world dataset.

CONCLUSION

The simulation approach to assessing malpractice risk mitigation through CDS was effective. Organizations should consider using malpractice claims experience to facilitate prioritizing CDS development.

Comparative outcome studies of clinical decision support software: limitations to the practice of evidence-based system acquisition

Clinical decision support systems (CDSSs) assist clinicians with patient diagnosis and treatment. However, inadequate attention has been paid to the process of selecting and buying systems. The diversity of CDSSs, coupled with research obstacles, marketplace limitations, and legal impediments, has thwarted comparative outcome studies and reduced the availability of reliable information and advice for purchasers. We review these limitations and recommend several comparative studies, which were conducted in phases; studies conducted in phases and focused on limited outcomes of safety, efficacy, and implementation in varied clinical settings. Additionally, we recommend the increased availability of guidance tools to assist purchasers with evidence-based purchases. Transparency is necessary in purchasers' reporting of system defects and vendors' disclosure of marketing conflicts of interest to support methodologically sound studies. Taken together, these measures can foster the evolution of evidence-based tools that, in turn, will enable and empower system purchasers to make wise choices and improve the care of patients.

Clinical Decision Support for Early Recognition of Sepsis

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

Decision time for clinical decision support systems

Clinical decision support systems are interactive software systems designed to help clinicians with decision-making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the computer science community, but their inner workings are less well understood by, and known to, clinicians. This article provides a brief explanation of clinical decision support systems and some examples of real-world systems. It also describes some of the challenges to implementing these systems in clinical environments and posits some reasons for the limited adoption of decision-support systems in practice. It aims to engage clinicians in the development of decision support systems that can meaningfully help with their decision-making tasks and to open a discussion about the future of automated clinical decision support as a part of healthcare delivery.

Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank

Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, we generated principal components for patients in our dataset, which were used to cluster patients into groups based on genetic ancestry. Consistent with other studies, we find a strong overall agreement (Kappa  = 0.872) between genetic ancestry and assigned race, with higher rates of agreement for African-descent and European-descent assignments, and reduced agreement for Hispanic, East Asian-descent, and South Asian-descent assignments. These results suggest caution when selecting study samples of non-African and non-European backgrounds when administratively-assigned race from biobanks is used.

The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis

BACKGROUND

The Health Information Technology for Economic and Clinical Health (HITECH) Act subsidizes implementation by hospitals of electronic health records with computerized provider order entry (CPOE), which may reduce patient injuries caused by medication errors (preventable adverse drug events, pADEs). Effects on pADEs have not been rigorously quantified, and effects on medication errors have been variable. The objectives of this analysis were to assess the effectiveness of CPOE at reducing pADEs in hospital-related settings, and examine reasons for heterogeneous effects on medication errors.

METHODS

Articles were identified using MEDLINE, Cochrane Library, Econlit, web-based databases, and bibliographies of previous systematic reviews (September 2013). Eligible studies compared CPOE with paper-order entry in acute care hospitals, and examined diverse pADEs or medication errors. Studies on children or with limited event-detection methods were excluded. Two investigators extracted data on events and factors potentially associated with effectiveness. We used random effects models to pool data.

RESULTS

Sixteen studies addressing medication errors met pooling criteria; six also addressed pADEs. Thirteen studies used pre-post designs. Compared with paper-order entry, CPOE was associated with half as many pADEs (pooled risk ratio (RR) = 0.47, 95% CI 0.31 to 0.71) and medication errors (RR = 0.46, 95% CI 0.35 to 0.60). Regarding reasons for heterogeneous effects on medication errors, five intervention factors and two contextual factors were sufficiently reported to support subgroup analyses or meta-regression. Differences between commercial versus homegrown systems, presence and sophistication of clinical decision support, hospital-wide versus limited implementation, and US versus non-US studies were not significant, nor was timing of publication. Higher baseline rates of medication errors predicted greater reductions (P < 0.001). Other context and implementation variables were seldom reported.

CONCLUSIONS

In hospital-related settings, implementing CPOE is associated with a greater than 50% decline in pADEs, although the studies used weak designs. Decreases in medication errors are similar and robust to variations in important aspects of intervention design and context. This suggests that CPOE implementation, as subsidized under the HITECH Act, may benefit public health. More detailed reporting of the context and process of implementation could shed light on factors associated with greater effectiveness.

Considerations for a successful clinical decision support system

Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.

Clinical decision support alert appropriateness: a review and proposal for improvement

BACKGROUND

Many healthcare providers are adopting clinical decision support (CDS) systems to improve patient safety and meet meaningful use requirements. Computerized alerts that prompt clinicians about drug-allergy, drug-drug, and drug-disease warnings or provide dosing guidance are most commonly implemented. Alert overrides, which occur when clinicians do not follow the guidance presented by the alert, can hinder improved patient outcomes.

METHODS

We present a review of CDS alerts and describe a proposal to develop novel methods for evaluating and improving CDS alerts that builds upon traditional informatics approaches. Our proposal incorporates previously described models for predicting alert overrides that utilize retrospective chart review to determine which alerts are clinically relevant and which overrides are justifiable.

RESULTS

Despite increasing implementations of CDS alerts, detailed evaluations rarely occur because of the extensive labor involved in manual chart reviews to determine alert and response appropriateness. Further, most studies have solely evaluated alert overrides that are appropriate or justifiable. Our proposal expands the use of web-based monitoring tools with an interactive dashboard for evaluating CDS alert and response appropriateness that incorporates the predictive models. The dashboard provides 2 views, an alert detail view and a patient detail view, to provide a full history of alerts and help put the patient's events in context.

CONCLUSION

The proposed research introduces several innovations to address the challenges and gaps in alert evaluations. This research can transform alert evaluation processes across healthcare settings, leading to improved CDS, reduced alert fatigue, and increased patient safety.

The association between use of a clinical decision support tool and adherence to monitoring for medication-laboratory guidelines in the ambulatory setting

BACKGROUND

Stage 2 Meaningful Use criteria require the use of clinical decision support systems (CDSS) on high priority health conditions to improve clinical quality measures. Although CDSS hold great promise, implementation has been fraught with challenges, evidence of their impact is mixed, and the optimal method of content delivery is unknown.

OBJECTIVE

The authors investigated whether implementation of a simple clinical decision support (CDS) tool was associated with improved prescriber adherence to national medication-laboratory monitoring guidelines for safety (hepatic function, renal function, myalgias/rhabdomyolysis) and intermediate outcomes for antidiabetic (Hemoglobin A(1c); HbA(1c)) and antihyperlipidemic (low density lipoprotein; LDL) medications prescribed within a diabetes registry.

METHODS

This was a retrospective observational study conducted in three phases of CDS implementation (2008-2009): pre-, transition-, and post-Prescriptions evaluated were ordered from an electronic health record within a multispecialty medical group. Adherence was evaluated within and without applying guideline-imposed time constraints.

RESULTS

Forty-thousand prescriptions were ordered over three timeframes. For hepatic and renal function, the proportion of prescriptions for which labs were monitored at any time increased from 52% to 65% (p<0.001); those that met time guidelines, from 14% to 21% (p<0.001). Only 6% of required labs were drawn to monitor for myalgias/rhabdomyolysis, regardless of timeframe. Over 90% of safety labs were within normal limits. The proportion of labs monitored at any time for LDL increased from 56% to 64% (p<0.001); those that met time guidelines from 11% to 17% (p<0.001). The proportion of labs monitored at any time for HbA(1c) remained the same (72%); those that met time guidelines decreased from 45% to 41% (p<0.001).

CONCLUSION

A simple CDS tool may be associated with improved adherence to guidelines. Efforts are needed to confirm findings and improve the timeliness of monitoring; investigations to optimize alerts should be ongoing.

Validation of computerized automatic calculation of the sequential organ failure assessment score

Purpose. To validate the use of a computer program for the automatic calculation of the sequential organ failure assessment (SOFA) score, as compared to the gold standard of manual chart review. Materials and Methods. Adult admissions (age > 18 years) to the medical ICU with a length of stay greater than 24 hours were studied in the setting of an academic tertiary referral center. A retrospective cross-sectional analysis was performed using a derivation cohort to compare automatic calculation of the SOFA score to the gold standard of manual chart review. After critical appraisal of sources of disagreement, another analysis was performed using an independent validation cohort. Then, a prospective observational analysis was performed using an implementation of this computer program in AWARE Dashboard, which is an existing real-time patient EMR system for use in the ICU. Results. Good agreement between the manual and automatic SOFA calculations was observed for both the derivation (N=94) and validation (N=268) cohorts: 0.02 ± 2.33 and 0.29 ± 1.75 points, respectively. These results were validated in AWARE (N=60). Conclusion. This EMR-based automatic tool accurately calculates SOFA scores and can facilitate ICU decisions without the need for manual data collection. This tool can also be employed in a real-time electronic environment.

Utilization of genetic testing prior to subspecialist referral for cerebellar ataxia

OBJECTIVE

To evaluate the utilization of laboratory testing in the diagnosis of cerebellar ataxia, including the completeness of initial standard testing for acquired causes, the early use of genetic testing, and associated clinical and nonclinical factors, among a cohort referred for subspecialty consultation.

METHODS

Data were abstracted from records of 95 consecutive ataxia patients referred to one neurogenetics subspecialist from 2006-2010 and linked to publicly available data on characteristics of referral clinicians. Multivariable logistic and linear regression models were used to analyze unique associations of clinical and nonclinical factors with laboratory investigation of acquired causes and with early genetic testing prior to referral.

RESULTS

At referral, 27 of 95 patients lacked evidence of any of 14 laboratory studies suggested for initial work-up of an acquired cause for ataxia (average number of tests=4.5). In contrast, 92% of patients had undergone brain magnetic resonance imaging prior to referral. Overall, 41.1% (n=39) had genetic testing prior to referral; there was no association between family history of ataxia and obtaining genetic testing prior to referral (p=0.39). The level of early genetic testing was 31.6%, primarily due to genetic testing despite an incomplete laboratory evaluation for acquired causes and no family history. A positive family history was consistently associated with less extensive laboratory testing (p=0.004), and referral by a neurologist was associated with higher levels of early genetic testing.

CONCLUSIONS

Among consecutive referrals to a single center, a substantial proportion of sporadic cases had genetic testing without evidence of a work-up for acquired causes. Better strategies to guide decision making and subspecialty referrals in rare neurologic disorders are needed, given the cost and consequences of genetic testing.