Determining correspondences between high-frequency MedDRA concepts and SNOMED: a case study

<p>Abstract</p> <p>Background</p> <p>The Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT) is being advocated as the foundation for encoding clinical documentation. While the electronic medical record is likely to play a critical role in pharmacovigilance - the detection of adverse events due to medications - classification and reporting of Adverse Events is currently based on the Medical Dictionary of Regulatory Activities (MedDRA). Complete and high-quality MedDRA-to-SNOMED CT mappings can therefore facilitate pharmacovigilance.</p> <p>The existing mappings, as determined through the Unified Medical Language System (UMLS), are partial, and record only one-to-one correspondences even though SNOMED CT can be used compositionally. Efforts to map previously unmapped MedDRA concepts would be most productive if focused on concepts that occur frequently in actual adverse event data.</p> <p>We aimed to identify aspects of MedDRA that complicate mapping to SNOMED CT, determine pattern in unmapped high-frequency MedDRA concepts, and to identify types of integration errors in the mapping of MedDRA to UMLS.</p> <p>Methods</p> <p>Using one years' data from the US Federal Drug Administrations Adverse Event Reporting System, we identified MedDRA preferred terms that collectively accounted for 95% of both Adverse Events and Therapeutic Indications records. After eliminating those already mapping to SNOMED CT, we attempted to map the remaining 645 Adverse-Event and 141 Therapeutic-Indications preferred terms with software assistance.</p> <p>Results</p> <p>All but 46 Adverse-Event and 7 Therapeutic-Indications preferred terms could be composed using SNOMED CT concepts: none of these required more than 3 SNOMED CT concepts to compose. We describe the common composition patterns in the paper. About 30% of both Adverse-Event and Therapeutic-Indications Preferred Terms corresponded to single SNOMED CT concepts: the correspondence was detectable by human inspection but had been missed during the integration process, which had created duplicated concepts in UMLS.</p> <p>Conclusions</p> <p>Identification of composite mapping patterns, and the types of errors that occur in the MedDRA content within UMLS, can focus larger-scale efforts on improving the quality of such mappings, which may assist in the creation of an adverse-events ontology.</p

Medically Complex Pediatric Patient Care Model: Coordinated Team-Based Care With Supporting Health Information Technology to Implement Best Practices and Address Care Gaps of Transitioning Age Patients and Family Caregivers

Background/Aims: Medically complex pediatric patients have multiple severe chronic conditions consuming disproportionate health care resources. The top 1% of Geisinger’s pediatric population uses health care representing 20% of total costs. Nationally, their needs are largely underserved by current delivery systems as they involve multiple specialists and settings, are challenging to coordinate, and place a tremendous burden on family caregivers. Providing effective care for children with special health care needs (CSHCN) requires a system that is integrated, comprehensive, coordinated and family-centered to foster positive experiences between families and providers. Advancing integrated systems of care for CSHCN and their families is a national priority and reflected in the Healthy People goals. Geisinger has developed a new innovative approach to CSHCN starting with ages 15 and over: the Medically Complex Pediatric Patient (MCPP) care model. Its objective is to address patient and family caregiver needs along with drivers of poor quality and unnecessarily high costs of care. This approach includes: (1) a comprehensive care clinic with an enhanced professional care team to provide and coordinate health care supported by (2) care bundles for reliable delivery of best practices and (3) advanced health information technology using Geisinger’s patient portal and Web-based applications to efficiently document and facilitate timely care, planning, management and coordination and promote good communication between families and providers. Methods: Development and implementation of the MCPP care model and an evaluation plan are occurring simultaneously. The evaluation plan begins with newly developed patient and family caregiver assessment questionnaires administered to all eligible families to collect baseline and postimplementation measures, with questions corresponding to multiple nationally validated surveys (National Survey of CSHCN, GAD-7, PHQ-9, Caregiver Strain Index). Results: Survey results of approximately 300 families will establish intensity of caregiving needs, caregiver challenges and ability to meet needs effectively, service quality and gaps consistent with national measures including CSHCN core outcomes that facilitate integrated systems of care for CSHCN such as care coordination, partners in decision-making, and medical home to assess preimplementation gaps and postimplementation performance. Discussion: Baseline results will be used to inform MCPP care model implementation and to assess its progress and performance over time consistent with national priorities

Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths

Background: Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs. Objectives: To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge. Research Design: Retrospective cohort study of admissions between June 2012 and June 2014. Subjects: All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system. Measures: All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. Results: The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively. Conclusions: The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs