Surgically-relevant quality of life thresholds for the Short Inflammatory Bowel Disease Questionnaire in Crohn's disease.

BACKGROUND: Despite growing interest in patient-reported outcome measures to track the progression of Crohn's disease, frameworks to apply these questionnaires in the preoperative setting are lacking. Using the Short Inflammatory Bowel Disease Questionnaire (sIBDQ), this study aimed to describe the interpretable quality of life thresholds and examine potential associations with future bowel resection in Crohn's disease. METHODS: Adult patients with Crohn's disease completing an sIBDQ at a clinic visit between 2020 and 2022 were eligible. A stoplight framework was adopted for sIBDQ scores, including a "Resection Red" zone suggesting poor quality of life that may benefit from discussions about surgery as well as a "Nonoperative Green" zone. Thresholds were identified with both anchor- and distribution-based methods using receiver operating characteristic curve analysis and subgroup percentile scores, respectively. To quantify associations between sIBDQ scores and subsequent bowel resection, multivariable logistic regression models were fit with covariates of age, sex assigned at birth, body mass index, medications, disease pattern and location, resection history, and the Harvey Bradshaw Index. The incremental discriminatory value of the sIBDQ beyond clinical factors was assessed through the area under the receiver operating characteristics curve (AUC) with an internal validation through bootstrap resampling. RESULTS: Of the 2003 included patients, 102 underwent Crohn's-related bowel resection. The sIBDQ Nonoperative Green zone threshold ranged from 61 to 64 and the Resection Red zone from 36 to 38. When adjusting for clinical covariates, a worse sIBDQ score was associated with greater odds of subsequent 90-day bowel resection when considered as a 1-point (odds ratio [OR] [95% CI], 1.05 [1.03-1.07]) or 5-point change (OR [95% CI], 1.27 [1.14-1.41]). Inclusion of the sIBDQ modestly improved discriminative performance (AUC [95% CI], 0.85 [0.85-0.86]) relative to models that included only demographics (0.57 [0.57-0.58]) or demographics with clinical covariates (0.83 [0.83-0.84]). CONCLUSION: In the decision-making process for bowel resection, disease-specific patient-reported outcome measures may be useful to identify patients with Crohn's disease with poor quality of life and promote a shared understanding of personalized burden.

Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record.

OBJECTIVE: To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR). METHODS: We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking information from structured EHR. We used 50 patients with a documented history of tobacco use for comparison and validation. RESULTS: 102,475 patients were included. The NLP-based approach achieved an F1-score of 0.909, and accuracy of 0.96. The baseline approach could identify 5,887 patients. Compared to the baseline approach, the number of identified patients using all structured data and the NLP-based algorithm was 7,194 (22.2 %) and 10,231 (73.8 %), respectively. The NLP-based approach identified 589 Black/African Americans, a significant increase of 119 %. CONCLUSION: We present a feasible NLP-based approach to identify LCS eligible patients. It provides a technical basis for the development of clinical decision support tools to potentially improve the utilization of LCS and diminish healthcare disparities.

Improved National Outcomes Achieved in a Cardiac Learning Health Collaborative Based on Early Performance Level.

OBJECTIVE: Within the National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC), a learning health network developed to improve outcomes for patients with hypoplastic left heart syndrome and variants, we assessed which centers contributed to reductions in mortality and growth failure. STUDY DESIGN: Centers within the NPC-QIC were divided into tertiles based on early performance for mortality and separately for growth failure. These groups were evaluated for improvement from the early to late time period and compared with the other groups in the late time period. RESULTS: Mortality was 3.8% for the high-performing, 7.6% for the medium-performing, and 14.4% for the low-performing groups in the early time period. Only the low-performing group had a significant change (P < .001) from the early to late period. In the late period, there was no difference in mortality between the high- (5.7%), medium- (7%), and low- (4.6%) performing centers (P = .5). Growth failure occurred in 13.9% for the high-performing, 21.9% for the medium-performing, and 32.8% for the low-performing groups in the early time period. Only the low-performing group had a significant change (P < .001) over time. In the late period, there was no significant difference in growth failure between the high- (19.8%), medium- (21.5%), and low- (13.5%) performing groups (P = .054). CONCLUSIONS: Improvements in the NPC-QIC mortality and growth measures are primarily driven by improvement in those performing the worst in these areas initially without compromising the success of high-performing centers. Focus for improvement may vary by center based on performance.

Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study.

OBJECTIVE: Developing effective and reliable rule-based clinical decision support (CDS) alerts and reminders is challenging. Using a previously developed taxonomy for alert malfunctions, we identified best practices for developing, testing, implementing, and maintaining alerts and avoiding malfunctions. MATERIALS AND METHODS: We identified 72 initial practices from the literature, interviews with subject matter experts, and prior research. To refine, enrich, and prioritize the list of practices, we used the Delphi method with two rounds of consensus-building and refinement. We used a larger than normal panel of experts to include a wide representation of CDS subject matter experts from various disciplines. RESULTS: 28 experts completed Round 1 and 25 completed Round 2. Round 1 narrowed the list to 47 best practices in 7 categories: knowledge management, designing and specifying, building, testing, deployment, monitoring and feedback, and people and governance. Round 2 developed consensus on the importance and feasibility of each best practice. DISCUSSION: The Delphi panel identified a range of best practices that may help to improve implementation of rule-based CDS and avert malfunctions. Due to limitations on resources and personnel, not everyone can implement all best practices. The most robust processes require investing in a data warehouse. Experts also pointed to the issue of shared responsibility between the healthcare organization and the electronic health record vendor. CONCLUSION: These 47 best practices represent an ideal situation. The research identifies the balance between importance and difficulty, highlights the challenges faced by organizations seeking to implement CDS, and describes several opportunities for future research to reduce alert malfunctions.

The Medicare Electronic Health Record Incentive Program: provider performance on core and menu measures.

OBJECTIVE: To measure performance by eligible health care providers on CMS's meaningful use measures. DATA SOURCE: Medicare Electronic Health Record Incentive Program Eligible Professionals Public Use File (PUF), which contains data on meaningful use attestations by 237,267 eligible providers through May 31, 2013. STUDY DESIGN: Cross-sectional analysis of the 15 core and 10 menu measures pertaining to use of EHR functions reported in the PUF. PRINCIPAL FINDINGS: Providers in the dataset performed strongly on all core measures, with the most frequent response for each of the 15 measures being 90-100 percent compliance, even when the threshold for a particular measure was lower (e.g., 30 percent). PCPs had higher scores than specialists for computerized order entry, maintaining an active medication list, and documenting vital signs, while specialists had higher scores for maintaining a problem list, recording patient demographics and smoking status, and for providing patients with an after-visit summary. In fact, 90.2 percent of eligible providers claimed at least one exclusion, and half claimed two or more. CONCLUSIONS: Providers are successfully attesting to CMS's requirements, and often exceeding the thresholds required by CMS; however, some troubling patterns in exclusions are present. CMS should raise program requirements in future years.

Matching identifiers in electronic health records: implications for duplicate records and patient safety.

OBJECTIVE: To quantify the percentage of records with matching identifiers as an indicator for duplicate or potentially duplicate patient records in electronic health records in five different healthcare organisations, describe the patient safety issues that may arise, and present solutions for managing duplicate records or records with matching identifiers. METHODS: For each institution, we retrieved deidentified counts of records with an exact match of patient first and last names and dates of birth and determined the number of patient records existing for the top 250 most frequently occurring first and last name pairs. We also identified methods for managing duplicate records or records with matching identifiers, reporting the adoption rate of each across institutions. RESULTS: The occurrence of matching first and last name in two or more individuals ranged from 16.49% to 40.66% of records; inclusion of date of birth reduced the rates to range from 0.16% to 15.47%. The number of records existing for the most frequently occurring name at each site ranged from 41 to 2552. Institutions varied widely in the methods they implemented for preventing, detecting and removing duplicate records, and mitigating resulting errors. CONCLUSIONS: The percentage of records having matching patient identifiers is high in several organisations, indicating that the rate of duplicate records or records may also be high. Further efforts are necessary to improve management of duplicate records or records with matching identifiers and minimise the risk for patient harm.

Clinical decision support for colon and rectal surgery: an overview.

Clinical decision support (CDS) has been shown to improve clinical processes, promote patient safety, and reduce costs in healthcare settings, and it is now a requirement for clinicians as part of the Meaningful Use Regulation. However, most evidence for CDS has been evaluated primarily in internal medicine care settings, and colon and rectal surgery (CRS) has unique needs with CDS that are not frequently described in the literature. The authors reviewed published literature in informatics and medical journals, combined with expert opinion to define CDS, describe the evidence for CDS, outline the implementation process for CDS, and present applications of CDS in CRS.CDS functionalities such as order sets, documentation templates, and order facilitation aids are most often described in the literature and most likely to be beneficial in CRS. Further research is necessary to identify and better evaluate additional CDS systems in the setting of CRS.