"Stealth" alerts to improve warfarin monitoring when initiating interacting medications

Information Technology-Based Interventions to Improve Drug-Drug Interaction Outcomes: A Systematic Review on Features and Effects

The purpose of this systematic review was to identify features and effects of information technology (IT)-based interventions on outcomes related to drug-drug interactions (DDI outcomes). A literature search was conducted in Medline, EMBASE, and the Cochrane Library for published English-language studies. Studies were included if a main outcome was related to DDIs, the intervention involved an IT-based system, and the study design was experimental or observational with controls. Study characteristics, including features and effects of IT-based interventions, were extracted. Nineteen studies comprising five randomized controlled trials (RCT), five non-randomized controlled trials (NRCT) and nine observational studies with controls (OWC) were included. Sixty-four percent of prescriber-directed interventions, and all non-prescriber interventions, were effective. Each of the following characteristics corresponded to groups of studies of which a majority were effective: automatic provision of recommendations within the providers' workflow, intervention at the time of decision-making, integration into other systems, and requiring the reason for not following the recommendations. Only two studies measured clinical outcomes: an RCT that showed no significant improvement and an OWC that showed improvement, but did not statistically assess the effect. Most studies that measured surrogate outcomes (e.g. potential DDIs) and other outcomes (e.g. adherence to alerts) showed improvements. IT-based interventions improve surrogate clinical outcomes and adherence to DDI alerts. However, there is lack of robust evidence about their effectiveness on clinical outcomes. It is recommended that researchers consider the identified features of effective interventions in the design of interventions and evaluate the effectiveness on DDI outcomes, particularly clinical outcomes.

Guidance for the practical management of warfarin therapy in the treatment of venous thromboembolism

Venous thromboembolism (VTE) is a serious and often fatal medical condition with an increasing incidence. The treatment of VTE is undergoing tremendous changes with the introduction of the new direct oral anticoagulants and clinicians need to understand new treatment paradigms. This article, initiated by the Anticoagulation Forum, provides clinical guidance based on existing guidelines and consensus expert opinion where guidelines are lacking. Well-managed warfarin therapy remains an important anticoagulant option and it is hoped that anticoagulation providers will find the guidance contained in this article increases their ability to achieve optimal outcomes for their patients with VTE Pivotal practical questions pertaining to this topic were developed by consensus of the authors and were derived from evidence-based consensus statements whenever possible. The medical literature was reviewed and summarized using guidance statements that reflect the consensus opinion(s) of all authors and the endorsement of the Anticoagulation Forum’s Board of Directors. In an effort to provide practical and implementable information about VTE and its treatment, guidance statements pertaining to choosing good candidates for warfarin therapy, warfarin initiation, optimizing warfarin control, invasive procedure management, excessive anticoagulation, subtherapeutic anticoagulation, drug interactions, switching between anticoagulants, and care transitions are provided.

Development, Evaluation and Implementation of Chief Complaint Groupings to Activate Data Collection: A Multi-Center Study of Clinical Decision Support for Children with Head Trauma

BACKGROUND

Overuse of cranial computed tomography scans in children with blunt head trauma unnecessarily exposes them to radiation. The Pediatric Emergency Care Applied Research Network (PECARN) blunt head trauma prediction rules identify children who do not require a computed tomography scan. Electronic health record (EHR) based clinical decision support (CDS) may effectively implement these rules but must only be provided for appropriate patients in order to minimize excessive alerts.

OBJECTIVES

To develop, implement and evaluate site-specific groupings of chief complaints (CC) that accurately identify children with head trauma, in order to activate data collection in an EHR.

METHODS

As part of a 13 site clinical trial comparing cranial computed tomography use before and after implementation of CDS, four PECARN sites centrally developed and locally implemented CC groupings to trigger a clinical trial alert (CTA) to facilitate the completion of an emergency department head trauma data collection template. We tested and chose CC groupings to attain high sensitivity while maintaining at least moderate specificity.

RESULTS

Due to variability in CCs available, identical groupings across sites were not possible. We noted substantial variability in the sensitivity and specificity of seemingly similar CC groupings between sites. The implemented CC groupings had sensitivities greater than 90% with specificities between 75-89%. During the trial, formal testing and provider feedback led to tailoring of the CC groupings at some sites.

CONCLUSIONS

CC groupings can be successfully developed and implemented across multiple sites to accurately identify patients who should have a CTA triggered to facilitate EHR data collection. However, CC groupings will necessarily vary in order to attain high sensitivity and moderate-to-high specificity. In future trials, the balance between sensitivity and specificity should be considered based on the nature of the clinical condition, including prevalence and morbidity, in addition to the goals of the intervention being considered.

Electronic risk assessment for venous thromboembolism: investigating physicians' rationale for bypassing clinical decision support recommendations

OBJECTIVE

The underutilisation of venous thromboembolism (VTE) prophylaxis is still a problem in the UK despite the emergence of national guidelines and incentives to increase the number of patients undergoing VTE risk assessments. Our objective was to examine the reasons doctors gave for not prescribing enoxaparin when recommended by an electronic VTE risk assessment alert.

DESIGN

We used a qualitative research design to conduct a thematic analysis of free text entered into an electronic prescribing system.

SETTING

The study took place in a large University teaching hospital, which has a locally developed electronic prescribing system known as PICS (Prescribing, Information and Communication System).

PARTICIPANTS

We extracted prescription data from all inpatient admissions over a 7-month period in 2012 using the audit database of PICS.

INTERVENTION

The completion of the VTE risk assessment form introduced into the hospital-wide electronic prescribing and health records system is mandatory. Where doctors do not prescribe VTE prophylaxis when recommended, they are asked to provide a reason for this decision. The free-text field was introduced in May 2012.

PRIMARY AND SECONDARY OUTCOME MEASURES

Free-text reasons for not prescribing enoxaparin when recommended were thematically coded.

RESULTS

A total of 1136 free-text responses from 259 doctors were collected in the time period and 1206 separate reasons were analysed and coded. 389 reasons (32.3%) for not prescribing enoxaparin were coded as being due to 'clinical judgment'; in 288 (23.9%) of the responses, doctors were going to reassess the patient or prescribe enoxaparin; and in 245 responses (20.3%), the system was seen to have produced an inappropriate alert.

CONCLUSIONS

In order to increase specificity of warnings and avoid users developing alert fatigue, it is essential that an evaluation of user responses and/or end user feedback as to the appropriateness and timing of alerts is obtained.

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

OBJECTIVE

Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems.

METHOD

We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: (1) Coding & Charge Entry and (2) Medical Information Services and two clinical areas: (3) Anesthesiology and (4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient's record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models.

RESULTS

The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas' associations were more distinguishable than others.

CONCLUSIONS

These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.