Background and Objective
On March 11, 2009, the Veterans Health Administration (VA) implemented an electronic health record (EHR)-based intervention that required all pathology results to be transmitted to ordering providers via mandatory automated notifications. We examined the impact of this intervention on improving follow-up of abnormal outpatient pathology results.
Research Design and Subjects
We extracted pathology reports from the EHR of two VA sites. From 16,738 pre- and 17,305 post-intervention reports between 09/01/2008 and 09/30/2009, we randomly selected about 5% and evaluated follow-up outcomes using a standardized chart review instrument. Documented responses to the alerted report (e.g., ordering follow-up tests or referrals, notifying patients, and prescribing/changing treatment) were recorded.
Primary outcome measures included proportion of timely follow-up responses (within 30 days) and median time to direct response for abnormal reports.
Of 816 pre- and 798 post-intervention reports reviewed, 666 (81.6%) and 688 (86.2%) were abnormal. Overall, there was no apparent intervention effect on timely follow-up (69% vs. 67.1%;p=0.4) or median time to direct response (8 days vs. 8 days; p=0.7). However, logistic regression uncovered a significant intervention effect (pre-intervention OR, 0.7; 95%CI 0.5-1.0) after accounting for site-specific differences in follow-up, with a lower likelihood of timely follow-up at one site (OR,0.4; 95%CI 0.2-0.7).
An electronic intervention to improve test result follow-up at two VA institutions using the same EHR was found effective only after accounting for certain local contextual factors. Aggregating the effect of EHR interventions across different institutions and EHRs without controlling for contextual factors might underestimate their potential benefits.
Anatomic pathology; electronic health record; communication; follow-up; post-analytic phase
Electronic health records are increasingly being used to facilitate referral communication in the outpatient setting. However, despite support by technology, referral communication between primary care providers and specialists is often unsatisfactory and is unable to eliminate care delays. This may be in part due to lack of attention to how information and communication technology fits within the social environment of health care. Making electronic referral communication effective requires a multifaceted “socio-technical” approach. Using an 8-dimensional socio-technical model for health information technology as a framework, we describe ten recommendations that represent good clinical practices to design, develop, implement, improve, and monitor electronic referral communication in the outpatient setting. These recommendations were developed on the basis of our previous work, current literature, sound clinical practice, and a systems-based approach to understanding and implementing health information technology solutions. Recommendations are relevant to system designers, practicing clinicians, and other stakeholders considering use of electronic health records to support referral communication.
We describe a novel, crowdsourcing method for generating a knowledge base of problem–medication pairs that takes advantage of manually asserted links between medications and problems.
Through iterative review, we developed metrics to estimate the appropriateness of manually entered problem–medication links for inclusion in a knowledge base that can be used to infer previously unasserted links between problems and medications.
Clinicians manually linked 231 223 medications (55.30% of prescribed medications) to problems within the electronic health record, generating 41 203 distinct problem–medication pairs, although not all were accurate. We developed methods to evaluate the accuracy of the pairs, and after limiting the pairs to those meeting an estimated 95% appropriateness threshold, 11 166 pairs remained. The pairs in the knowledge base accounted for 183 127 total links asserted (76.47% of all links). Retrospective application of the knowledge base linked 68 316 medications not previously linked by a clinician to an indicated problem (36.53% of unlinked medications). Expert review of the combined knowledge base, including inferred and manually linked problem–medication pairs, found a sensitivity of 65.8% and a specificity of 97.9%.
Crowdsourcing is an effective, inexpensive method for generating a knowledge base of problem–medication pairs that is automatically mapped to local terminologies, up-to-date, and reflective of local prescribing practices and trends.
Electronic health records; decision support systems; clinical; knowledge bases; medication systems; hospital; data collection; clinical decision support; error management and prevention; evaluation; monitoring and surveillance; ADEs; developing/using computerized provider order entry; knowledge representations; classical experimental and quasi-experimental study methods (lab and field); designing usable (responsive) resources and systems; statistical analysis of large datasets; electronic health records; clinical summarization; user interface; patient preferences; patient-centered; heart failure; psychological; nursing; clinical information systems
The purpose of this study was to identify recommended practices for computerized clinical decision support (CDS) development and implementation and for knowledge management (KM) processes in ambulatory clinics and community hospitals using commercial or locally developed systems in the U.S.
Guided by the Multiple Perspectives Framework, the authors conducted ethnographic field studies at two community hospitals and five ambulatory clinic organizations across the U.S. Using a Rapid Assessment Process, a multidisciplinary research team: gathered preliminary assessment data; conducted on-site interviews, observations, and field surveys; analyzed data using both template and grounded methods; and developed universal themes. A panel of experts produced recommended practices.
The team identified ten themes related to CDS and KM. These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support. Experts developed recommendations about each theme. The original Multiple Perspectives framework was modified to make explicit a new theoretical construct, that of Translational Interaction.
These ten themes represent areas that need attention if a clinic or community hospital plans to implement and successfully utilize CDS. In addition, they have implications for workforce education, research, and national-level policy development. The Translational Interaction construct could guide future applied informatics research endeavors.
The US FDA has been collecting information on medical devices involved in significant adverse advents since 1984. These reports have been used by researchers to advise clinicians on potential risks and complications of using these devices.
Research adverse events related to the use of Clinical Information Systems (CIS) as reported in FDA databases.
Three large, national, adverse event medical device databases were examined for reports pertaining to CIS.
One hundred and twenty unique reports (from over 1.4 million reports) were found, representing 32 manufacturers. The manifestations of these adverse events included: missing or incorrect data, data displayed for the wrong patient, chaos during system downtime and system unavailable for use. Analysis of these reports illustrated events associated with system design, implementation, use, and support.
The identified causes can be used by manufacturers to improve their products and by clinical facilities and providers to adjust their workflow and implementation processes appropriately. The small number of reports found indicates a need to raise awareness regarding publicly available tools for documenting problems with CIS and for additional reporting and dialog between manufacturers, organizations, and users.
Electronic Health Records; Information Systems; Mandatory Reporting; Medical Errors; United States Food and Drug Administration
Comparative Effectiveness Research (CER) has the potential to transform the current healthcare delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for inter-institutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast six, large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, socio-technical model of health information technology use to help guide our work. We identified six generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.
Methods; Comparative Effectiveness Research; Organization and Administration; Medical Informatics; Methods
Successful subspecialty referrals require considerable coordination and interactive communication among the primary care provider (PCP), the subspecialist, and the patient, which may be challenging in the outpatient setting. Even when referrals are facilitated by electronic health records (EHRs) (i.e., e-referrals), lapses in patient follow-up might occur. Although compelling reasons exist why referral coordination should be improved, little is known about which elements of the complex referral coordination process should be targeted for improvement. Using Okhuysen & Bechky's coordination framework, this paper aims to understand the barriers, facilitators, and suggestions for improving communication and coordination of EHR-based referrals in an integrated healthcare system.
We conducted a qualitative study to understand coordination breakdowns related to e-referrals in an integrated healthcare system and examined work-system factors that affect the timely receipt of subspecialty care. We conducted interviews with seven subject matter experts and six focus groups with a total of 30 PCPs and subspecialists at two tertiary care Department of Veterans Affairs (VA) medical centers. Using techniques from grounded theory and content analysis, we identified organizational themes that affected the referral process.
Four themes emerged: lack of an institutional referral policy, lack of standardization in certain referral procedures, ambiguity in roles and responsibilities, and inadequate resources to adapt and respond to referral requests effectively. Marked differences in PCPs' and subspecialists' communication styles and individual mental models of the referral processes likely precluded the development of a shared mental model to facilitate coordination and successful referral completion. Notably, very few barriers related to the EHR were reported.
Despite facilitating information transfer between PCPs and subspecialists, e-referrals remain prone to coordination breakdowns. Clear referral policies, well-defined roles and responsibilities for key personnel, standardized procedures and communication protocols, and adequate human resources must be in place before implementing an EHR to facilitate referrals.
Electronic health records (EHRs) have potential quality and safety benefits. However, reports of EHR-related safety hazards are now emerging. The Office of the National Coordinator (ONC) for Health Information Technology (HIT) recently sponsored an Institute of Medicine committee to evaluate how HIT use affects patient safety. In this paper, we propose the creation of a national EHR oversight program to provide dedicated surveillance of EHR-related safety hazards and to promote learning from identified errors, close calls, and adverse events. The program calls for data gathering, investigation/analysis and regulatory components. The first two functions will depend on institution-level EHR safety committees that will investigate all known EHR-related adverse events and near-misses and report them nationally using standardized methods. These committees should also perform routine safety self-assessments to proactively identify new risks. Nationally, we propose the long-term creation of a centralized, non-partisan board with an appropriate legal and regulatory infrastructure to ensure the safety of EHRs. We discuss the rationale of the proposed oversight program and its potential organizational components and functions. These include mechanisms for robust data collection and analyses of all safety concerns using multiple methods that extend beyond reporting; multidisciplinary investigation of selected high-risk safety events; and enhanced coordination with other national agencies in order to facilitate broad dissemination of hazards information. Implementation of this proposed infrastructure can facilitate identification of EHR-related adverse events and errors and potentially create a safer and more effective EHR-based health care delivery system.
Despite its promise, recent literature has revealed possible safety hazards of health information technology (HIT) use. The Office of the National Coordinator for HIT recently sponsored an Institute of Medicine committee to synthesize evidence and experience from the field on how HIT affects patient safety. To lay the groundwork for defining, measuring, and analyzing HIT-related safety hazards, we propose that Health information technology-related error occurs anytime HIT is unavailable for use, malfunctions during use, is used incorrectly by someone, or when HIT interacts with another system component incorrectly, resulting in data being lost or incorrectly entered, displayed, or transmitted. These errors, or the decisions that result from them, significantly increase the risk of adverse events and patient harm. In this paper, we describe how a socio-technical approach can be used to understand the complex origins of HIT errors, which may have roots in rapidly evolving technological, professional, organizational, and policy initiatives.
Electronic Health Records; Health Information Technology; Patient Safety; Errors
Notifying clinicians about abnormal test results through electronic health record (EHR) -based "alert" notifications may not always lead to timely follow-up of patients. We sought to understand barriers, facilitators, and potential interventions for safe and effective management of abnormal test result delivery via electronic alerts.
We conducted a qualitative study consisting of six 6-8 member focus groups (N = 44) at two large, geographically dispersed Veterans Affairs facilities. Participants included full-time primary care providers, and personnel representing diagnostic services (radiology, laboratory) and information technology. We asked participants to discuss barriers, facilitators, and suggestions for improving timely management and follow-up of abnormal test result notifications and encouraged them to consider technological issues, as well as broader, human-factor-related aspects of EHR use such as organizational, personnel, and workflow.
Providers reported receiving a large number of alerts containing information unrelated to abnormal test results, many of which were believed to be unnecessary. Some providers also reported lacking proficiency in use of certain EHR features that would enable them to manage alerts more efficiently. Suggestions for improvement included improving display and tracking processes for critical alerts in the EHR, redesigning clinical workflow, and streamlining policies and procedures related to test result notification.
Providers perceive several challenges for fail-safe electronic communication and tracking of abnormal test results. A multi-dimensional approach that addresses technology as well as the many non-technological factors we elicited is essential to design interventions to reduce missed test results in EHRs.
Decision Support Systems; Clinical; Automated notification; diagnostic errors; abnormal diagnostic test results; Medical Records Systems; Computerized; patient follow-up; patient safety; health information technology; communication; primary care
We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commercially-available EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined "meaningful use" criteria.
We developed and fielded a 17-question survey to representatives from nine commercially available EHR vendors and four leading internally developed EHRs. The first part of the survey asked basic questions about the vendor's EHR. The second part asked specifically about the CDS-related system tools and capabilities that each vendor provides. The final section asked about clinical content.
All of the vendors and institutions have multiple modules capable of providing clinical decision support interventions to clinicians. The majority of the systems were capable of performing almost all of the key knowledge management functions we identified.
If these well-designed commercially-available systems are coupled with the other key socio-technical concepts required for safe and effective EHR implementation and use, and organizations have access to implementable clinical knowledge, we expect that the transformation of the healthcare enterprise that so many have predicted, is achievable using commercially-available, state-of-the-art EHRs.
The United States (US) Health Information Technology for Economic and Clinical Health Act of 2009 has spurred adoption of electronic health records. The corresponding meaningful use criteria proposed by the Centers for Medicare and Medicaid Services mandates use of computerized provider order entry (CPOE) systems. Yet, adoption in the US and other Western countries is low and descriptions of successful implementations are primarily from the inpatient setting; less frequently the ambulatory setting. We describe prescriber and staff perceptions of implementation of a CPOE system for medications (electronic- or e-prescribing system) in the ambulatory setting.
Using a cross-sectional study design, we conducted eight focus groups at three primary care sites in an independent medical group. Each site represented a unique stage of e-prescribing implementation - pre/transition/post. We used a theoretically based, semi-structured questionnaire to elicit physician (n = 17) and staff (n = 53) perceptions of implementation of the e-prescribing system. We conducted a thematic analysis of focus group discussions using formal qualitative analytic techniques (i.e. deductive framework and grounded theory). Two coders independently coded to theoretical saturation and resolved discrepancies through discussions.
Ten themes emerged that describe perceptions of e-prescribing implementation: 1) improved availability of clinical information resulted in prescribing efficiencies and more coordinated care; 2) improved documentation resulted in safer care; 3) efficiencies were gained by using fewer paper charts; 4) organizational support facilitated adoption; 5) transition required time; resulted in workload shift to staff; 6) hardware configurations and network stability were important in facilitating workflow; 7) e-prescribing was time-neutral or time-saving; 8) changes in patient interactions enhanced patient care but required education; 9) pharmacy communications were enhanced but required education; 10) positive attitudes facilitated adoption.
Prescribers and staff worked through the transition to successfully adopt e-prescribing, and noted the benefits. Overall impressions were favorable. No one wished to return to paper-based prescribing.
Health information technology and electronic medical records (EMRs) are potentially powerful systems-based interventions to facilitate diagnosis and treatment because they ensure the delivery of key new findings and other health related information to the practitioner. However, effective communication involves more than just information transfer; despite a state of the art EMR system, communication breakdowns can still occur. [1-3] In this project, we will adapt a model developed by the Systems Engineering Initiative for Patient Safety (SEIPS) to understand and improve the relationship between work systems and processes of care involved with electronic communication in EMRs. We plan to study three communication activities in the Veterans Health Administration's (VA) EMR: electronic communication of abnormal imaging and laboratory test results via automated notifications (i.e., alerts); electronic referral requests; and provider-to-pharmacy communication via computerized provider order entry (CPOE).
Our specific aim is to propose a protocol to evaluate the systems and processes affecting outcomes of electronic communication in the computerized patient record system (related to diagnostic test results, electronic referral requests, and CPOE prescriptions) using a human factors engineering approach, and hence guide the development of interventions for work system redesign.
This research will consist of multiple qualitative methods of task analysis to identify potential sources of error related to diagnostic test result alerts, electronic referral requests, and CPOE; this will be followed by a series of focus groups to identify barriers, facilitators, and suggestions for improving the electronic communication system. Transcripts from all task analyses and focus groups will be analyzed using methods adapted from grounded theory and content analysis.
Objectives: Computer-based provider order entry (CPOE) systems are implemented to increase both efficiency and accuracy in health care, but these systems often cause a myriad of emotions to arise. This qualitative research investigates the emotions surrounding CPOE implementation and use.
Methods: We performed a secondary analysis of several previously collected qualitative data sets from interviews and observations of over 50 individuals. Three researchers worked in parallel to identify themes that expressed emotional responses to CPOE. We then reviewed and classified these quotes using a validated hierarchical taxonomy of semantically homogeneous terms associated with specific emotions.
Results: The implementation and use of CPOE systems provoked examples of positive, negative, and neutral emotions. Negative emotional responses were the most prevalent, by far, in all the observations.
Conclusion: Designing and implementing CPOE systems is difficult. These systems and the implementation process itself often inspire intense emotions. If designers and implementers fail to recognize that various CPOE features and implementation strategies can increase clinicians' negative emotions, then the systems may fail to become a routine part of the clinical care delivery process. We might alleviate some of these problems by designing positive feedback mechanisms for both the systems and the organizations.
Real-time clinical decision support (CDS) integrated into clinicians' workflow has the potential to profoundly affect the cost, quality, and safety of health care delivery. Recent reports have identified a surprisingly low acceptance rate for different types of CDS. We hypothesized that factors affecting CDS system acceptance could be categorized as relating to differences in patients, physicians, CDS-type, or environmental characteristics.
We conducted a survey of all adult primary care physicians (PCPs, n = 225) within our group model Health Maintenance Organization (HMO) to identify factors that affect their acceptance of CDS. We defined clinical decision support broadly as "clinical information" that is either provided to you or accessible by you, from the clinical workstation (e.g., enhanced flow sheet displays, health maintenance reminders, alternative medication suggestions, order sets, alerts, and access to any internet-based information resources).
110 surveys were returned (49%). There were no differences in the age, gender, or years of service between those who returned the survey and the entire adult PCP population. Overall, clinicians stated that the CDS provided "helps them take better care of their patients" (3.6 on scale of 1:Never – 5:Always), "is worth the time it takes" (3.5), and "reminds them of something they've forgotten" (3.2). There was no difference in the perceived acceptance rate of alerts based on their type (i.e., cost, safety, health maintenance). When asked about specific patient characteristics that would make the clinicians "more", "equally" or "less" likely to accept alerts: 41% stated that they were more (8% stated "less") likely to accept alerts on elderly patients (> 65 yrs); 38% were more (14% stated less) likely to accept alerts on patients with more than 5 current medications; and 38% were more (20% stated less) likely to accept alerts on patients with more than 5 chronic clinical conditions. Interestingly, 80% said they were less likely to accept alerts when they were behind schedule and 84% of clinicians admitted to being at least 20 minutes behind schedule "some", "most", or "all of the time".
Even though a majority of our clinical decision support suggestions are not explicitly followed, clinicians feel they are of benefit and would be even more beneficial if they had more time available to address them.
American public policy makers recently established the goal of providing the majority of Americans with electronic health records by 2014. This will require a National Health Information Infrastructure (NHII) that is far more complete than the one that is currently in its formative stage of development. We describe a conceptual framework to help measure progress toward that goal.
The NHII comprises a set of clusters, such as Regional Health Information Organizations (RHIOs), which, in turn, are composed of smaller clusters and nodes such as private physician practices, individual hospitals, and large academic medical centers. We assess progress in terms of the availability and use of information and communications technology and the resulting effectiveness of these implementations. These three attributes can be studied in a phased approach because the system must be available before it can be used, and it must be used to have an effect. As the NHII expands, it can become a tool for evaluating itself.
The NHII has the potential to transform health care in America – improving health care quality, reducing health care costs, preventing medical errors, improving administrative efficiencies, reducing paperwork, and increasing access to affordable health care. While the President has set an ambitious goal of assuring that most Americans have electronic health records within the next 10 years, a significant question remains "How will we know if we are making progress toward that goal?" Using the definitions for "nodes" and "clusters" developed in this article along with the resulting measurement framework, we believe that we can begin a discussion that will enable us to define and then begin making the kinds of measurements necessary to answer this important question.
Email is the most important mechanism introduced since the telephone for developing interpersonal relationships. This study was designed to provide insight into how patients are using email to request information or services from their healthcare providers.
Following IRB approval, we reviewed all electronic mail (e-mail) messages sent between five study clinicians and their patients over a one-month period. We used a previously described taxonomy of patient requests to categorize all patient requests contained in the messages. We measured message volume, frequency, length and response time for all messages sent to and received by these clinicians.
On average the 5 physicians involved in this study received 40 messages per month, each containing approximately 139 words. Replies sent by the physicians contained 39 words on average and 59.4% of them were sent within 24 hours. Patients averaged 1 request per message. Requests for information on medications or treatments, specific symptoms or diseases, and requests for actions regarding medications or treatments accounted for 75% of all requests. Physicians fulfilled 80.2% of all these requests. Upon comparison of these data to that obtained from traditional office visits, it appears that the potential exists for email encounters to substitute for some percentage of office visits.
Electronic messaging is an important method for physicians and patients to communicate and further develop their relationship. While many physicians worry that either the number or length of messages from their patients will overwhelm them, there is no evidence to support this. In fact, the evidence suggests that many patient requests, formerly made over the telephone or during office visits, can be addressed via email thus potentially saving both patients and physicians time.
Bar coded medication administration (BCMA), the automated electronic verification of medications by nurses at the patient bedside, provides an additional layer of safety to the process of medication administration in the hospital setting. We performed a retrospective, descriptive study of BCMA alerts for elevated potassium (>5.5 mg/dL) in place within a multihospital healthcare system. Overall, 642 BCMA alerts were analyzed with a 21.3% acceptance rate. In subgroup analysis, we found that the BCMA acceptance rate was 6.9% for patients aged less than one year, and 85.6% for patients aged greater than one year. The major contributing factor to the low overall acceptance rate was the high frequency of alerts in patients less than 1 year of age. Modifications to rules logic may be necessary for this specific population. While BCMA alerts can beneficial, they should be carefully implemented with periodic post-implementation analysis and refinement.
Following the IOM report, “Health IT and Patient Safety: Building Safer Systems for Better Care,” the Office of the National Coordinator for Health Information Technology sponsored a project to address safety concerns in electronic health record-enabled (EHR) healthcare systems. To address the complexity of EHR-related errors and the difficulty in eliminating them, we designed the SAFER project (Safety Assurance Factors for EHR Resilience) to proactively identify potential safety issues and best practices for addressing them. We take into account the full sociotechnical context of EHR implementation and use. Our iterative work is grounded in several recently developed informatics-based scientific methods including:
Review of scientific clinical informatics literatureUse of Rapid Assessment Process mixed-methods approaches developed for evaluating EHR-enabled healthcare systems in contextA semantic wiki for asynchronous collaborationAn 8-dimension socio-technical model of safe and effective EHR use
We are developing and piloting self-assessment “checklist-type” tools using a unique mix of methods based on the science of biomedical informatics. As the country continues its rapid EHR deployment, we believe that these tools are essential to ensure that the safety of the “EHR-enabled healthcare system” continues to improve.
Using an eight-dimensional model for studying socio-technical systems, a multidisciplinary team of investigators identified barriers and facilitators to clinical decision support (CDS) implementation in a community setting, the Mid-Valley Independent Physicians Association in the Salem, Oregon area. The team used the Rapid Assessment Process, which included nine formal interviews with CDS stakeholders, and observation of 27 clinicians. The research team, which has studied 21 healthcare sites of various sizes over the past 12 years, believes this site is an excellent example of an organization which is using a commercially available electronic-health-record system with CDS well. The eight-dimensional model proved useful as an organizing structure for the evaluation.
Developing/using computerized provider order entry; improving the education and skills training of health professionals; developing/using clinical decision support (other than diagnostic) and guideline systems; social/organizational study; qualitative/ethnographic field study; knowledge representations; classical experimental and quasi-experimental study methods (lab and field); designing usable (responsive) resources and systems; statistical analysis of large datasets; discovery and text and data mining methods; automated learning; human–computer interaction and human-centered computing; qualitative/ethnographic field study; clincal decision support; machine learning; knowledge bases; clinical decision support; ambulatory care; ambulatory care
Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems.
To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs.
Study design and methods
We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4).
Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common.
We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.
Developing/using computerized provider order entry; knowledge representations; classical experimental and quasi-experimental study methods (lab and field); designing usable (responsive) resources and systems; statistical analysis of large datasets; discovery; text and data mining methods; automated learning; human-computer interaction and human-centered computing; qualitative/ethnographic field study; clinical decision support; manning maddux; decision support; biomedical informatics; developing and refining EHR data standards (including image standards); controlled terminologies and vocabularies; measuring/improving patient safety and reducing medical errors; machine learning; electronic health records; meaningful use