A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems.

Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

Variation in high-priority drug-drug interaction alerts across institutions and electronic health records.

Objective: The United States Office of the National Coordinator for Health Information Technology sponsored the development of a "high-priority" list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive). Materials and methods: We conducted evaluations of electronic health records (EHRs) at a convenience sample of health care organizations across the United States using a standardized testing protocol with simulated orders. Results: Evaluations of 19 systems were conducted at 13 sites using 14 different EHRs. Across systems, 69% of the high-priority DDI pairs produced alerts. Implementation and display of the DDI alerts tested varied between systems, even when the same EHR vendor was used. Across the drug pairs evaluated, implementation and display of DDI alerts differed, ranging from 27% (4/15) to 93% (14/15) implementation. Discussion: Currently, there is no standard of care covering which DDI alerts to implement or how to display them to providers. Opportunities to improve DDI alerting include using differential displays based on DDI severity, establishing improved lists of clinically significant DDIs, and thoroughly reviewing organizational implementation decisions regarding DDIs. Conclusion: DDI alerting is clinically important but not standardized. There is significant room for improvement and standardization around evidence-based DDIs.

A survey of factors affecting clinician acceptance of clinical decision support.

BACKGROUND: 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. METHODS: 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). RESULTS: 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". CONCLUSION: 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.

Effectiveness of an electronic medical record clinical quality alert prepared by off-line data analysis.

We tested whether off-line data analysis, instead of event monitoring, was a viable method for initiating a clinical quality alert. A cohort of patients eligible for an alert was identified by off-line data analysis and a flag was set in their ambulatory Electronic Medical Records. One hundred clinicians were randomly assigned either to a control group or to a group that received the alert when viewing the electronic medical record of eligible patients. Primarily due to actions of their clinicians, 315 of the 580 patients (54.3%) seen by alerted clinicians were no longer eligible for the alert at the end of the one month study, compared to 128 of the 496 patients (25.8%) seen by control clinicians (p<.001). When not alerted, Allied Health clinicians were less likely than physicians to prescribe aspirin, but they responded similarly to the alert. There were no differences in response by specialty or gender of the clinician. Off-line data analysis proved to be an effective method of initiating a clinical alert.

Use of order sets in inpatient computerized provider order entry systems: a comparative analysis of usage patterns at seven sites.

BACKGROUND: Many computerized provider order entry (CPOE) systems include the ability to create electronic order sets: collections of clinically related orders grouped by purpose. Order sets promise to make CPOE systems more efficient, improve care quality and increase adherence to evidence-based guidelines. However, the development and implementation of order sets can be expensive and time-consuming and limited literature exists about their utilization. METHODS: Based on analysis of order set usage logs from a diverse purposive sample of seven sites with commercially and internally developed inpatient CPOE systems, we developed an original order set classification system. Order sets were categorized across seven non-mutually exclusive axes: admission/discharge/transfer (ADT), perioperative, condition-specific, task-specific, service-specific, convenience, and personal. In addition, 731 unique subtypes were identified within five axes: four in ADT (S=4), three in perioperative, 144 in condition-specific, 513 in task-specific, and 67 in service-specific. RESULTS: Order sets (n=1914) were used a total of 676,142 times at the participating sites during a one-year period. ADT and perioperative order sets accounted for 27.6% and 24.2% of usage respectively. Peripartum/labor, chest pain/acute coronary syndrome/myocardial infarction and diabetes order sets accounted for 51.6% of condition-specific usage. Insulin, angiography/angioplasty and arthroplasty order sets accounted for 19.4% of task-specific usage. Emergency/trauma, obstetrics/gynecology/labor delivery and anesthesia accounted for 32.4% of service-specific usage. Overall, the top 20% of order sets accounted for 90.1% of all usage. Additional salient patterns are identified and described. CONCLUSION: We observed recurrent patterns in order set usage across multiple sites as well as meaningful variations between sites. Vendors and institutional developers should identify high-value order set types through concrete data analysis in order to optimize the resources devoted to development and implementation.

Order sets in computerized physician order entry systems: an analysis of seven sites.

Most computerized physician order entry (CPOE) systems have built-in support for order sets (collections of orders grouped by a clinical purpose). Evidence and experience suggest that order sets are important tools for ordering efficiency and decision support and may influence ordering. Developing and maintaining order sets is costly, so hospitals often must prioritize which order sets can be created. We analyzed order set utilization at seven diverse sites with CPOE. The number of order sets per site ranged from 81 to 535, and the number of order set uses per discharge ranged from 0.48 to 9.89. We also compared the top ten order sets at each site, and found many commonalities, such as generic and condition-specific admission order sets, surgical sets and clinical pathways. We also found that, at each site, utilization of order sets was skewed, with a small number of order sets comprising the bulk of utilization. These findings may be useful for order sets developers, particularly in settings where resources are constrained and the most important order sets must be developed first.

Clinician's assessments of outpatient electronic medical record alert and reminder usability and usefulness requirements.

Electronic medical record alerts and reminders are increasingly employed as a means of decreasing medical errors and increasing the quality and cost-effectiveness of care. However, clinicians indicate that alerts and reminders can be either help or hindrance. Discerning the elements that determine which they will be, and the requirements of a helpful alert or reminder, was the focus of this study. We convened three focus groups, comprised of a total of 16 participants. During analysis, five themes emerged: Efficiency, Usefulness, Information Content, User Interface, and Workflow. In addition there were some New Ideas and Surprises. Specific usability and usefulness requirements emerged from within the themes and these are described.