Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research

Krasowski, Matthew D.; Schriever, Andy; Mathur, Gagan; Blau, John; Stauffer, Stephanie L; Ford, Bradley

doi:10.4103/2153-3539.161615

Cited by 19 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data in an MDW are available in an architecture conducive to rapid searching. 19 These data are regularly updated, stored in a structured and consistent manner, quality checked, and linked to facilitate cross-table querying and analysis. Because data in an MDW reside in a database maintained separately from the source production information system databases (eg, the EHR), MDW data can be accessed and used at the same time as production information systems 19 without slowing production system response time or otherwise compromising production systems’ requirements for real-time data access.…”

mentioning

confidence: 99%

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

Raebel

Quintana

Schroeder

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

View full text Add to dashboard Cite

Context.— The laboratory total testing process includes preanalytic, analytic, and postanalytic phases, but most laboratory quality improvement efforts address the analytic phase. Expanding quality improvement to preanalytic and postanalytic phases via use of medical data warehouses, repositories that include clinical, utilization, and administrative data, can improve patient care by ensuring appropriate test utilization. Cross-department, multidisciplinary collaboration to address gaps and improve patient and system outcomes is beneficial. Objective.— To demonstrate medical data warehouse utility for characterizing laboratory-associated quality gaps amenable to preanalytic or postanalytic interventions. Design.— A multidisciplinary team identified quality gaps. Medical data warehouse data were queried to characterize gaps. Organizational leaders were interviewed about quality improvement priorities. A decision aid with elements including national guidelines, local and national importance, and measurable outcomes was completed for each gap. Results.— Gaps identified included (1) test ordering; (2) diagnosis, detection, and documentation, and (3) high-risk medication monitoring. After examination of medical data warehouse data including enrollment, diagnoses, laboratory, pharmacy, and procedures for baseline performance, high-risk medication monitoring was selected, specifically alanine aminotransferase, aspartate aminotransferase, complete blood count, and creatinine testing among patients receiving disease-modifying antirheumatic drugs. The test utilization gap was in monitoring timeliness (eg, >60% of patients had a monitoring gap exceeding the guideline recommended frequency). Other contributors to selecting this gap were organizational enthusiasm, regulatory labeling, and feasibility of a significant laboratory role in addressing the gap. Conclusions.— A multidisciplinary process facilitated identification and selection of a laboratory medicine quality gap. Medical data warehouse data were instrumental in characterizing gaps.

show abstract

mentioning

confidence: 99%

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

Raebel

Quintana

Schroeder

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

View full text Add to dashboard Cite

show abstract

“…23,24 For more sophisticated searches, a data warehouse (Starmaker, Park Street Solutions, Naperville, Illinois) was used; this database was available to pathology faculty and trainees. 25 This warehouse allowed for queries that are either very difficult or even infeasible within Epic Reporting Workbench or other routinely available informatics resources. An additional resource for challenging queries was Healthcare Enterprise Decision Intelligence (HEDI), a data warehouse that had restricted access limited mostly to specialized hospital information technology (IT) staff.…”

Section: Methodsmentioning

confidence: 99%

Using Focused Laboratory Management and Quality Improvement Projects to Enhance Resident Training and Foster Scholarship

et al. 2017

Self Cite

View full text Add to dashboard Cite

Training in patient safety, quality, and management is widely recognized as an important element of graduate medical education. These concepts have been intertwined in pathology graduate medical education for many years, although training programs face challenges in creating explicit learning opportunities in these fields. Tangibly involving pathology residents in management and quality improvement projects has the potential to teach and reinforce key concepts and further fulfill Accreditation Council for Graduate Medical Education goals for pursuing projects related to patient safety and quality improvement. In this report, we present our experience at a pathology residency program (University of Iowa) in engaging pathology residents in projects related to practical issues of laboratory management, process improvement, and informatics. In this program, at least 1 management/quality improvement project, typically performed during a clinical chemistry/management rotation, was required and ideally resulted in a journal publication. The residency program also initiated a monthly management/informatics series for pathology externs, residents, and fellows that covers a wide range of topics. Since 2010, all pathology residents at the University of Iowa have completed at least 1 management/quality improvement project. Many of the projects involved aspects of laboratory test utilization, with some projects focused on other areas such as human resources, informatics, or process improvement. Since 2012, 31 peer-reviewed journal articles involving effort from 26 residents have been published. Multiple projects resulted in changes in ongoing practice, particularly within the hospital electronic health record. Focused management/quality improvement projects involving pathology residents can result in both meaningful quality improvement and scholarly output.

show abstract

“…201410771). Utilizing the data‐mining tool Starmaker, the EMRs of patients at the University of Iowa Hospitals and Clinics were mined to identify all patients with non‐zero HbS levels from 2010 through 2015. One hundred ninety‐two patients were identified with non‐zero HbS values during that period.…”

Section: Methodsmentioning

confidence: 99%

Predicting changes in hemoglobin S after simple transfusion using complete blood counts

2017

Self Cite

View full text Add to dashboard Cite

BACKGROUND Hemoglobin S percentages are used in the management of patients who have sickle cell disease. However, hemoglobin S measurements often are not routinely or rapidly performed. Rapid and accurate methods to estimate hemoglobin S levels after simple transfusion may improve the care of patients with sickle cell disease. STUDY DESIGN AND METHODS A comprehensive review of the electronic medical record identified 24 stable patients with sickle cell disease who received simple red blood cell transfusions and had hemoglobin S measurements before and after the transfusion that were less than 72 hours apart. Examination of these patients identified 62 separate transfusions that met our criteria. Three simple equations that utilized complete blood count values and readily available information from the medical record were used to predict the post‐transfusion hemoglobin S level after transfusion (Equation 1: predicted post‐transfusion hemoglobin = pre‐transfusion hemoglobin S × [pre‐transfusion hemoglobin/post‐transfusion hemoglobin]; Equation 2: predicted post‐transfusion hemoglobin S = pre‐transfusion hemoglobin S × [pre‐transfusion hematocrit/post‐transfusion hematocrit]; and Equation 3: predicted post‐transfusion hemoglobin S = pre‐transfusion hemoglobin S × total pre‐transfusion hemoglobin/[total pre‐transfusion hemoglobin + (red blood cell volume × 20)]). RESULTS The predicted hemoglobin S values for all three equations showed a highly significant correlation with the measured post‐hemoglobin S value. The coefficient of determination (R2) for Equations 1, 2, and 3 was 0.95, 0.92, and 0.97, respectively. Predicting the post‐transfusion hemoglobin S value using estimates of the patient's total hemoglobin and the transfused hemoglobin (Equation 3) was the most precise. CONCLUSION Reductions in hemoglobin S values in patients with sickle cell disease who receive simple red blood cell transfusions can be reliably predicted using complete blood cell measurements and simple arithmetic equations.

show abstract

Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research

Cited by 19 publications

References 30 publications

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

Using Focused Laboratory Management and Quality Improvement Projects to Enhance Resident Training and Foster Scholarship

Predicting changes in hemoglobin S after simple transfusion using complete blood counts

Contact Info

Product

Resources

About