Next generation terminology infrastructure to support interprofessional care planning.

OBJECTIVE: Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics. DESIGN: The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models. RESULTS: Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. LIMITATIONS: This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. CONCLUSIONS: Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients.

Discontinuation of statins in routine care settings: a cohort study.

BACKGROUND: Systematic data on discontinuation of statins in routine practice of medicine are limited. OBJECTIVE: To investigate the reasons for statin discontinuation and the role of statin-related events (clinical events or symptoms believed to have been caused by statins) in routine care settings. DESIGN: A retrospective cohort study. SETTING: Practices affiliated with Brigham and Women's Hospital and Massachusetts General Hospital in Boston. PATIENTS: Adults who received a statin prescription between 1 January 2000 and 31 December 2008. MEASUREMENTS: Information on reasons for statin discontinuations was obtained from a combination of structured electronic medical record entries and analysis of electronic provider notes by validated software. RESULTS: Statins were discontinued at least temporarily for 57 292 of 107 835 patients. Statin-related events were documented for 18 778 (17.4%) patients. Of these, 11 124 had statins discontinued at least temporarily; 6579 were rechallenged with a statin over the subsequent 12 months. Most patients who were rechallenged (92.2%) were still taking a statin 12 months after the statin-related event. Among the 2721 patients who were rechallenged with the same statin to which they had a statin-related event, 1295 were receiving the same statin 12 months later, and 996 of them were receiving the same or a higher dose. LIMITATIONS: Statin discontinuations and statin-related events were assessed in practices affiliated with 2 academic medical centers. Utilization of secondary data could have led to missing or misinterpreted data. Natural-language-processing tools used to compensate for the low (30%) proportion of reasons for statin discontinuation documented in structured electronic medical record fields are not perfectly accurate. CONCLUSION: Statin-related events are commonly reported and often lead to statin discontinuation. However, most patients who are rechallenged can tolerate statins long-term. This suggests that many of the statin-related events may have other causes, are tolerable, or may be specific to individual statins rather than the entire drug class. PRIMARY FUNDING SOURCE: National Library of Medicine, Diabetes Action Research and Education Foundation, and Chinese National Key Program of Clinical Science.

Towards a standardized representation to support data reuse: representing the ICNP semantics using the HL7 RIM.

Representing nursing assessment data in a reusable manner is important as it provides a basis for decision making in patient care. In a previous study, we have extended the ICNP concept model to support representation of nursing assessment data. In this study, we evaluated its potential to support electronic documentation of nursing assessment data and HL7 conformant message generation by mapping it to the HL7 RIM. The semantics represented by the ICNP were completely related to the RIM implying that the ICNP is a good candidate terminology to encode the data with in an electronic documentation system. A few differences between the two models in representing the same semantics suggests that there is value to supporting broad ranges of semantic relations in the ICNP.

Development of a Process and Infrastructure to Outreach Stakeholders for Capturing Healthcare System Stress in Emergency Response Situations.

Real time data provided by frontline clinicians could be used to direct immediate resources during a public health emergency and inform increased preparedness for future events. The United States Critical Illness and Injury Trials Group Program for Emergency Preparedness (USCIIT-PREP), a group of expert critical care and emergency medicine physicians at various academic medical centers across the US, aims to enhance the national capability of rapid electronic data collection, along with analysis and dissemination of findings. To achieve these aims, USCIIT-PREP created a process for real-time data capture that relies on a curated and engaged network of clinical providers from various geographical regions to respond to short online "Pulse" queries about healthcare system stress. During a period of three years, five queries were created and distributed. The first two queries were used to develop and validate the data collection infrastructure. Results are reported for the last three queries between June 2015 and March 2016. Response rates consistently ranged from 39% to 42%. Our team demonstrated that our system and processes were ready for creation and rapid dissemination of episodic queries for rapid data collection, transmittal, and analysis through a curated national network of clinician responders during a public health emergency. USCIIT-PREP aims to further increase the response rate through additional engagement efforts within the network, to continue to grow the clinician responder database, and to optimize additional query content.

Gap Analysis and Refinement Recommendations of Skin Alteration and Pressure Ulcer Enterprise Reference Models against Nursing Flowsheet Data Elements.

Reference models are an essential instrument to provide structure and guidance in the creation and use of data elements within an organizations' electronic health record (EHR). Standardization of data elements is imperative to ensure clinical data is consistently and reliably captured for use in clinical documentation, care communication, and a variety of downstream data uses. Ongoing assessment and refinement of reference models and data elements are necessary to ascertain clinical data capture is applicable and inclusive across a variety of caregivers and domains. We performed a gap analysis on current state nursing data elements against two validated interprofessional reference models: skin alteration and pressure ulcer assessments. We present our findings along with recommendations for reference model refinements. We also highlight additional findings of inconsistencies and redundancies within data elements used for nursing documentation and highlight recommendations for improvement.

Evolution of an Implementation-Ready Interprofessional Pain Assessment Reference Model.

Standards to increase consistency of comprehensive pain assessments are important for safety, quality, and analytics activities, including meeting Joint Commission requirements and learning the best management strategies and interventions for the current prescription Opioid epidemic. In this study we describe the development and validation of a Pain Assessment Reference Model ready for implementation on EHR forms and flowsheets. Our process resulted in 5 successive revisions of the reference model, which more than doubled the number of data elements to 47. The organization of the model evolved during validation sessions with panels totaling 48 subject matter experts (SMEs) to include 9 sets of data elements, with one set recommended as a minimal data set. The reference model also evolved when implemented into EHR forms and flowsheets, indicating specifications such as cascading logic that are important to inform secondary use of data.

Design and implementation of an application and associated services to support interdisciplinary medication reconciliation efforts at an integrated healthcare delivery network.

Confusion about patients' medication regimens during the hospital admission and discharge process accounts for many preventable and serious medication errors. Many organizations have begun to redesign their clinical processes to address this patient safety concern. Partners HealthCare, an integrated delivery network in Boston, Massachusetts, has answered this interdisciplinary challenge by leveraging its multiple outpatient electronic medical records (EMR) and inpatient computerized provider order entry (CPOE) systems to facilitate the process of medication reconciliation. This manuscript describes the design of a novel application and the associated services that aggregate medication data from EMR and CPOE systems so that clinicians can efficiently generate an accurate pre-admission medication list. Information collected with the use of this application subsequently supports the writing of admission and discharge orders by physicians, performance of admission assessment by nurses, and reconciliation of inpatient orders by pharmacists. Results from early pilot testing suggest that this new medication reconciliation process is well accepted by clinicians and has significant potential to prevent medication errors during transitions of care.

Prioritization and Refinement of Clinical Data Elements within EHR Systems.

Standardization of clinical data element (CDE) definitions is foundational to track, interpret, and analyze patient states, populations, and costs across providers, settings and time - critical activities to achieve the Triple Aim: improving the experience of care, improving the health of populations, and reducing per capita healthcare costs. We defined and implemented two analytical methods to prioritize and refine CDE definitions within electronic health records (EHRs), taking into account resource restrictions to carry out the analysis and configuration changes: 1) analysis of downstream data needs to identify high priority clinical topics, and 2) gap analysis of EHR CDEs when compared to reference models for the same clinical topics. We present use cases for six clinical topics. Pain Assessment and Skin Alteration Assessment were topics with the highest regulatory and non-regulatory downstream data needs and with significant gaps across documention artifacts in our system, confirming that these topics should be refined first.

A Practical Approach to Governance and Optimization of Structured Data Elements.

Definition and configuration of clinical content in an enterprise-wide electronic health record (EHR) implementation is highly complex. Sharing of data definitions across applications within an EHR implementation project may be constrained by practical limitations, including time, tools, and expertise. However, maintaining rigor in an approach to data governance is important for sustainability and consistency. With this understanding, we have defined a practical approach for governance of structured data elements to optimize data definitions given limited resources. This approach includes a 10 step process: 1) identification of clinical topics, 2) creation of draft reference models for clinical topics, 3) scoring of downstream data needs for clinical topics, 4) prioritization of clinical topics, 5) validation of reference models for clinical topics, and 6) calculation of gap analyses of EHR compared against reference model, 7) communication of validated reference models across project members, 8) requested revisions to EHR based on gap analysis, 9) evaluation of usage of reference models across project, and 10) Monitoring for new evidence requiring revisions to reference model.