Michael Krall

How to Design Computerized Alerts to Ensure Safe Prescribing Practices

Article-at-a-Glance Background Medication errors and preventable adverse drug events are common, and about half of medication errors occur during medication ordering. This study was designed to develop and evaluate medication safety alerts and processes for educating prescribers about the alerts. Methods At Kaiser Permanente Northwest, a group-model health maintenance organization where prescribers have used computerized order entry since 1996, qualitative interviews were conducted with 20 primary care prescribers. Results Prescribers considered alerts helpful for providing prescribing and preventive health information. More than half the interviewees stated that it would be unwise to let clinicians control or avoid safety alerts. Common frustrations were (1) being delayed by the alert, (2) having difficulty interpreting the alert, and (3) receiving the same alert repeatedly. Most prescribers preferred small-group educational sessions tied to existing meetings and having local physicians conduct education sessions. Discussion The findings were used to design a strategy for introducing and promoting the interventions, modifying the alert text and tools, and focusing the education on how clinicians could use the alerts effectively.

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.

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.

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.

Comparison of computer-based clinical decision support systems and content for diabetes mellitus

BACKGROUND Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. OBJECTIVE We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. METHODS We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. RESULTS The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. CONCLUSION Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

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.

Big Data and Population-Based Decision Support

As we enter the realm in which many laboratory and quantitative parameters can be accumulated about a patient, and where natural language processing methods are becoming more capable of extracting and codifying data from unstructured narrative reports, the matching of patients to smaller subgroups is beginning to be more broadly feasible. In the future, population-based decision support is likely to be much more patient-specific and much more reliant on predictive models for specific subpopulations. This chapter reviews the current state and trends in applying clinical decision support to population management and population-derived data to decision support for the individual patient.

Definition of a metadata model for a multi–layered clinical practice guideline representation framework

Representatives from a consortium of academic institutions, healthcare organisations, and commercial entities have collaborated to implement and evaluate a multi–layered representation framework targeted to promote the sharing and distribution of clinical practice guideline knowledge artefacts. Definition of a metadata model for knowledge artefacts submitted to a shared repository based on this framework proceeded through an iterative process that sought to strike a balance between the need to provide enough specification to allow for efficient indexing and retrieval and recognition of the potential burden imposed by excessive requirements.

Commentary on Echoes of silence: Death and denial

not!Therearedoubtlessmanyexplanationsfor why the situationdeveloped as it did and,in retrospect,we might identifycountlesspointsin theprocess whereboth doctorsandfamily memberscouldhave—andprobablyshould have— behaveddifferently.But all of us who have worked with dying patientsand their families know that blamingdoctorsis aperfectlycommon responsetotherageandfrustrationwe experiencewhen someonewe loveis dying. Whatevidencedo wehavethat this caseis sodramaticallydifferent? Theresponses ofCarolSheehanand JackRichmanbothermefarmorethan Sedman’spainful narrative.Why do thesetwo professionalsblithely assumethatwhatevertheyreadaboutincompetenceor insensitivitybymedical personnelis objectivelyfactual. Why do theytakefor grantedthat it is doctorsandotherswhoareatfault,without evena suggestionthat perhapsMs. Sedman’sfamilybroughtits ownbaggageto the encounterswith Dr. SurgeonandDr. Pancreas?Andwhy assume that the family’s failure to get help the first time they contacted hospicewas the fault of the intake worker or anyone’s ‘fault?’ Surely Sheehanand Richmanare aware of howpeopledistortandmuddletheirrequeststo the extent that one is often renderedpowerlessto help them, despite every good intention. Ms. Sedman’snarrativemakesabundantly clearhowdifficult it musthavebeento decipher thisfamily’s needs. Andfinally, it isunfair(aswellasnaive)tojudge this family’s experiencewithout havingheardfrom the peoplewhom theyaccuseof denial,insensitivityand callousness. Thesecommentsarenot a defense of doctorsandotherswhodo nottake the time and make the effort to ‘be there’ with the patients and families when they face seriousillness and death.We all havemetthem,mostof ushavetangledwith them andfew of usarepreparedto forgive them.And, in fact, if DeniseSedman’sdescription of herfamily’s agonyservesto further sensitizeusto ourtasksascaretakers, all the better.Butwemustbecautious of the hubris which borderson selfrighteousnessandinfectsmanyin our field: we almostgleefully believethe mostoutrageousstoriesbecausethey serveto reinforceourownbiasesand inflatedsenseof ourownrectitude.To assumethata distraught,grievingsurvivor is objectively and completely reportingreality is anerror of professionaljudgment.This isnotto saythat theAmericanJournalofHospiceCare shouldnothavepublishedtheSedman piece.It wasworth readingandposes thoughtfulandvaluablequestions. But in our responseto it I would much ratherseeusapplyourcriticalfaculties to more thoughtful analysesand not resortto facile flnger-pointing.LJ