Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results.

BACKGROUND: Early detection of prediabetes and management of cardiovascular (CV) risk factors to prevent CV disease is essential, but clinicians are often slow to address this risk. Clinical decision support (CDS) systems, with appropriate implementation, can potentially improve prediabetes identification and treatment. METHODS/DESIGN: 34 Midwestern primary care clinics were randomized to receive or not receive access to a prediabetes (PreD) CDS tool. Between October 2016 and December 2019, primary care clinicians (PCPs) received Pre-D CDS alerts during visits with adult patients identified with prediabetes and who met minimal inclusion criteria and had at least one CV risk factor not at goal. The PCP Pre-D CDS included a summary of six modifiable CV risk factors and patient-specific treatment recommendations. Study outcomes included total modifiable CV risk, six modifiable CV risk factors, use of CV medications, and referrals. The Consolidated Framework for Implementation Research was used to examine CDS implementation processes. DISCUSSION: This cluster-randomized pragmatic trial allowed PCPs the opportunity to improve CV risk in a timely manner for patients with prediabetes. Effectiveness will be assessed using an intent-to-treat analysis. Implementation processes and outcomes will be assessed through interviews, surveys, and electronic health record data harvested by the CDS tool itself. Pre-implementation interviews and activities identified key strategies to incorporate as part of the Pre-D CDS implementation process to ensure acceptability and high use rates. Analyses are ongoing and trial results are expected in mid-2021.

Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing.

OBJECTIVES: Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse. DESIGN: Using a retrospective cohort of 3,668 chronic noncancer pain patients with at least one opioid agreement between January 1, 2007, and December 31, 2012, we examined the availability of electronic health record structured and unstructured data to populate the Opioid Risk Tool (ORT) and other selected outcomes. Clinician-documented opioid agreement violations in the clinical notes were determined using NLP techniques followed by manual review of the notes. RESULTS: Confirmed through manual review, the NLP algorithm had 96.1% sensitivity, 92.8% specificity, and 92.6% positive predictive value in identifying opioid agreement violation. At the time of most recent opioid agreement, automated ORT identified 42.8% of patients as at low risk, 28.2% as at moderate risk, and 29.0% as at high risk for opioid abuse. During a year following the agreement, 22.5% of patients had opioid agreement violations. Patients classified as high risk were three times more likely to violate opioid agreements compared with those with low/moderate risk. CONCLUSION: Our findings suggest that NLP techniques have potential utility to support clinicians in screening chronic noncancer pain patients considered for long-term opioid therapy.

Managing personal health information in distributed research network environments.

BACKGROUND: Studying rare outcomes, new interventions and diverse populations often requires collaborations across multiple health research partners. However, transferring healthcare research data from one institution to another can increase the risk of data privacy and security breaches. METHODS: A working group of multi-site research programmers evaluated the need for tools to support data security and data privacy. The group determined that data privacy support tools should: 1) allow for a range of allowable Protected Health Information (PHI); 2) clearly identify what type of data should be protected under the Health Insurance Portability and Accountability Act (HIPAA); and 3) help analysts identify which protected health information data elements are allowable in a given project and how they should be protected during data transfer. Based on these requirements we developed two performance support tools to support data programmers and site analysts in exchanging research data. RESULTS: The first tool, a workplan template, guides the lead programmer through effectively communicating the details of multi-site programming, including how to run the program, what output the program will create, and whether the output is expected to contain protected health information. The second performance support tool is a checklist that site analysts can use to ensure that multi-site program output conforms to expectations and does not contain protected health information beyond what is allowed under the multi-site research agreements. CONCLUSIONS: Together the two tools create a formal multi-site programming workflow designed to reduce the chance of accidental PHI disclosure.

Managing protected health information in distributed research network environments: automated review to facilitate collaboration.

BACKGROUND: Multi-site health sciences research is becoming more common, as it enables investigation of rare outcomes and diseases and new healthcare innovations. Multi-site research usually involves the transfer of large amounts of research data between collaborators, which increases the potential for accidental disclosures of protected health information (PHI). Standard protocols for preventing release of PHI are extremely vulnerable to human error, particularly when the shared data sets are large. METHODS: To address this problem, we developed an automated program (SAS macro) to identify possible PHI in research data before it is transferred between research sites. The macro reviews all data in a designated directory to identify suspicious variable names and data patterns. The macro looks for variables that may contain personal identifiers such as medical record numbers and social security numbers. In addition, the macro identifies dates and numbers that may identify people who belong to small groups, who may be identifiable even in the absences of traditional identifiers. RESULTS: Evaluation of the macro on 100 sample research data sets indicated a recall of 0.98 and precision of 0.81. CONCLUSIONS: When implemented consistently, the macro has the potential to streamline the PHI review process and significantly reduce accidental PHI disclosures.