Automated Clinical Trial Cohort Definition and Evaluation with CQL and CDS-Hooks.

BACKGROUND: Patient recruitment for clinical trials faces major challenges with current methods being costly and often requiring time-consuming acquisition of medical histories and manual matching of potential subjects. OBJECTIVES: Designing and implementing an Electronic Health Record (EHR) and domain-independent automation architecture using Clinical Decision Support (CDS) standards that allows researchers to effortlessly enter standardized trial criteria to retrieve eligibility statistics and integration into a clinician workflow to automatically trigger evaluation without added clinician workload. METHODS: Cohort criteria are translated into the Clinical Quality Language (CQL) and integrated into Measures and CDS-Hooks for patient- and population-level evaluation. RESULTS: Successful application of simplified real-world trial criteria to Fast Healthcare Interoperability Resources (FHIR®) test data shows the feasibility of obtaining individual patient eligibility and trial details as well as population eligibility statistics and a list of qualifying patients. CONCLUSION: Employing CDS standards for automating cohort definition and evaluation shows promise in streamlining patient selection, aligning with increasing legislative demands for standardized healthcare data.

The Impact of Obesity on Early Complications Following Tracheostomy: An Evaluation of a Nationwide Registry.

INTRODUCTION: The obesity epidemic is an important public health problem in the United States. Previous studies have revealed the association between obesity and various surgical complications. Tracheostomy which is an important lifesaving procedure may prove technically challenging in an obese patient. This study sought to evaluate the association between obesity and early complications following standard tracheostomy using a national registry. METHODS: Adult patients who underwent tracheostomy from 2007 to 2017 were analyzed using the Nationwide Inpatient Sample (NIS). The population was stratified into obese and non-obese groups. Early complications following standard tracheostomy were identified and compared between the two groups. Multivariable logistic regression analyses were performed to assess the association between obesity and early complications following tracheostomy. RESULTS: Data pertaining to 205 032 adult patients were evaluated. Obese patients accounted for 12.1% (n = 21 816) of the entire cohort. The most common complication in the cohort was perioperative bleeding (4316 [2.1%]). A total of 1382 (0.67%), 949 (0.46%), and 134 (0.07%) patients developed pneumothorax/pneumomediastinum, stoma/surgical site infection, and tracheal injury following standard tracheostomy, respectively. There was no difference in the odds of tracheal injury, perioperative bleeding, and pneumomediastinum/pneumothorax following standard tracheostomy in the obese and non-obese group in multivariable analysis. However, obesity was associated with 60% increased odds of developing stoma/surgical site infection following standard tracheostomy (OR 1.60 [1.33-1.92], P < 0.01). CONCLUSION: Obesity is associated with an increased risk of developing stoma/surgical site infection following standard tracheostomy. This adds to the growing need for measures to help curb the obesity epidemic in a bid to improve surgical outcomes.

From Authoring to Evaluating an Electronic Health Quality Measure - Applying Logic to FHIR® with CQL for Calculating Immunization Coverage.

BACKGROUND: Current monitoring and evaluation methods challenge the healthcare system. Specifically for the use case of immunization coverage calculation, person-level data retrieval is required instead of inaccurate aggregation methods. The Clinical Quality Language (CQL) by HL7®, has the potential to overcome current challenges by offering an automated generation of quality reports on top of an HL7® FHIR® repository. OBJECTIVES: This paper provides a method to author and evaluate an electronic health quality measure as demonstrated by a proof-of-concept on immunization coverage calculation. METHODS: Five artifact types were identified to transform unstructured input into CQL, to define the terminology, to create test data, and to evaluate the new quality measures. RESULTS: CQL logic and FHIR® test data were created and evaluated by using the different approaches of manual evaluation, unit testing in the HAPI FHIR project, as well as showcasing the functionality with a developed user interface for immunization coverage analysis. CONCLUSION: Simple, powerful, and transparent evaluations on a small population can be achieved with existing open-source tools, by applying CQL logic to FHIR®.

Reply to "On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 'Towards the Use of Standardised Terms in Clinical Case Studies for Process Mining in Healthcare' ".

Many thanks to Dr. Mordaunt for his thoughtful Comment, which we were delighted to read with great interest[...].

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare.

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.