Summary Background Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care. Objective To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development. Methods We adopted as guiding principles to (a) capture data as a by product of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed—either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM)—were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined “grains” from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-generated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week “sprints” for rapid-cycle feedback and refinement. Results Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends. Conclusions This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development,...

Objective Rheumatoid arthritis (RA) disease activity assessment is critical for treatment decisions and treat to target (T2T) outcomes. Utilization of the electronic medical record (EMR) and techniques to improve the routine capture of disease activity measures in clinical practice are not well described. We leveraged a Lean Six Sigma (LSS) approach, a data‐driven five‐step process improvement and problem‐solving methodology, coupled with EMR modifications to evaluate improvement in disease activity documentation and patient outcomes. Methods A RA registry was established, and structured fields for Routine Assessment of Patient Index Data (RAPID3) and Clinical Disease Activity Index (CDAI) were built in the EMR, along with a dashboard to display provider performance rates. An initial rapid‐cycle improvement intervention was launched, and subsequent LSS improvement cycles helped in standardization of clinic workflow, modifying provider behaviors, and motivating better documentation practices. Trends related to CDAI score categories were compared over time using run charts. Results Our project included 1322 patients with RA and 10 241 encounters between April 2016 and December 2019. Initially, RAPID3 completion rates increased from 16% to 50%, and CDAI from 15% to 44% from the RCI intervention. Post LSS intervention, the RAPID3 rate increased to more than 90% (sustained at 85%), and CDAI rate increased to more than 80% (sustained at 72%). The patients in the low disease/remission category increased from 54% to 66% (p < 0.001), and those in the high disease category decreased from 15% to 7% (p < 0.001), demonstrating improved T2T outcomes. Conclusion Combining EMR modifications with systems redesign utilizing LSS approach led to impressive and sustained improvement in disease activity documentation and T2T outcomes.

two time periods. 56% reduced readmission rates for Ischemic Stroke performance and 47% reduced readmission rates for Hemorrhagic Stroke performance.Notes (figure 1): Analysis excludes 0-17 age group and includes the neurosciences and spine service lines and the Brain/Central Nervous System (CNS) Cancer CARE Family from the cancer service line. CNS injury includes concussion, late effects of neuro trauma, paralysis, skull fracture and major brain injury, and spinal cord injury. Movement disorders include Parkinson disease, movement disorders, multiple sclerosis and demyelinating diseases. Neuro pain disorders include headache/migraine, neuro pain and neuropathy. Other includes hydrocephalus and spina bifida, neurologic disease-other, and sleep disorders. Stroke and neurovascular include ischemic and hemorrhagic stroke, transient ischemic attack, and neurovascular diseases. Sources:

Depression is a common and serious illness that impairs the health of individuals and societies globally. It is associated with a significant economic burden, with productivity losses exceeding $40 billion dollars annually in the United States (U.S.) alone. This project focused on the use of a systematic, data-driven approach to improve the screening rate for depression in an academic, metropolitan cancer center located in North Texas. A multidisciplinary team collaboratively applied Lean Six Sigma education, methods, and tools within oncology and psychiatry clinics to address the increased risk of depression among oncology patients. Improving the standardization of screening and follow-up processes, resulted in a 44% sustained increase in the depression screening and follow-up performance rate. This improvement was verified to be statistically significant through the use of control charts toward the end of the project.