Performance Measures Using Electronic Health Records: Five Case Studies

Presents the experiences of five provider organizations in developing, testing, and implementing four types of electronic quality-of-care indicators based on EHR data. Discusses challenges, and compares results with those from traditional indicators

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

Platelet Counts and Coagulation Tests Prior to Neuraxial Anesthesia in Patients With Preeclampsia: A Retrospective Analysis

This retrospective, descriptive study aimed to assess hematologic testing practices in 100 patients with preeclampsia undergoing neuraxial blockade (NB). Prior to NB, platelet (PLT) count was performed in 61 (98%) of 62 women in labor and in 37 (97%) of 38 women undergoing cesarean delivery (CD). No patients had a pre-NB PLT count 12 hours. The lack of consistency in pre-NB coagulation testing and the variable time intervals between laboratory tests and NB may be due to a lack of consensus among anesthesiologists for determining “safe” hemostatic conditions for NB placement in patients with preeclampsia

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence &lt; 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients

Representing and Retrieving Patients\u27 Falls Risk Factors and Risk for Falls Among Adults in Acute Care Through the Electronic Health Record

Defining fall risk factors and predicting fall risk status among patients in acute care has been a topic of research for decades. With increasing pressure on hospitals to provide quality care and prevent hospital-acquired conditions, the search for effective fall prevention interventions continues. Hundreds of risk factors for falls in acute care have been described in the literature. However, due to variations in the terms utilized to represent each fall risk factor, an effort to compare findings across settings and replicate research is hampered. As the expectations for the effective use of electronic health records increase, an opportunity exists to create infrastructure within clinical information systems, constructed with evidence-based knowledge and standardized terms, that will support interoperability between systems and enable comparative research. The purpose of this study is to identify to what extent selected fall risk factors and the problem, `risk for falls\u27 are represented and retrievable, in patients\u27 electronic health record, in one acute care setting. Specifically, this study sought to answer three questions: 1) How can the selected fall risk factors and the problem, `risk for falls\u27 be represented through selected standardized terminologies? 2) How are the selected fall risk factors and problem, `risk for falls\u27 represented in a clinical information system? and 3) Which of the selected fall risk factors and problem, `risk for falls\u27 can be retrieved from the electronic health record? The study was guided by the Knowledge Based Nursing Initiative (KBNI) framework. The study was conducted at a local health system within the hospital division, utilizing electronic, patient clinical data. Five selected fall risk factors and the problem, `risk for falls,\u27 were mapped to five standardized terminologies utilizing lexical matching. The terms mapped from the five terminologies were compared to the terms, located in discrete fields within the study site\u27s clinical information system. In addition to SNOMED CT and ICD-9 CM terms, a mixture of vendor and site-specific terms that represented the problem, `risk for falls,\u27 and the five selected fall risk factors were located in the study site\u27s clinical information system. The mapped ICD-9 CM terms and fourteen of the twenty-two SNOMED CT terms were located in the `Problem List\u27 and `Medical History\u27 sections of the clinical information system, while the vendor and site-specific terms were located in `Orders,\u27 `Nursing Flow Sheet,\u27 and `Rehabilitation Flow Sheet\u27 sections. Although both the ICD-9 CM and SNOMED CT terminologies were visible to the clinicians, one of the two mapped SNOMED CT terms representing the problem, `risk for falls,\u27 and fourteen of the twenty-two mapped fall risk factors were not visible because they did not correspond to ICD-9 CM terms. Site-specific terms representing `cognitive impairment\u27 and `impaired gait\u27 were located in both the `Nursing Flow Sheet\u27 and `Rehabilitation Flow Sheet\u27 section. While the terms were lexically similar, the terms were not exact matches and the machine-readable codes differed.Data recorded in 995 episodes of care were retrieved from the electronic data warehouse for analysis. While the SNOMED CT terms were not available for retrieval from the electronic data warehouse, the ICD-9 CM, vendor, and site-specific terms were available. As there were not SNOMED CT terms available for retrieval from the electronic data warehouse, the representation of the problem, `risk for falls,\u27 was not retrievable as a standardized term; however, it was retrieved as a Morse Fall Scale score of 40 or greater among 64.7% of the sample. The percentage of the five fall risk factors represented with the ICD-9 CM terms was lower than the percentage of fall risk factors represented with vendor and site-specific terms. While it is promising that two standardized terminologies have been embedded in the study site\u27s system, limiting the SNOMED CT terms to those that have corresponding ICD-9 terms limits the representation of both the problem, `risk for falls,\u27 and the five selected fall risk factors. It is recommended that hospital administrators embed standardized terminologies in their entirety to allow for adequate representation of terms. Accepting terminologies in their entirety would allow for interoperability between health systems and enable comparative research. Additionally, if vendor and site-specific terms are embedded, clinical information analysts in partnership with clinicians should assure that terms representing the same clinical data (e.g., disorientation), match across different sections of the clinical information system or a cross-mapping of those terms exist in order to support interoperability within the system

Tennessee\u27s Annual Overdose Report 2019, Understanding and Responding to the Opioid

https://digitalcommons.memphis.edu/govpubs-tn-dept-health-drug-poisonings-in-tennessee/1017/thumbnail.jp

Risk Prediction of a Multiple Sclerosis Diagnosis

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Although medications have been developed to slow the disease progression and help manage symptoms, MS research has yet to result in a cure. Early diagnosis and treatment of the disease have been shown to be effective at slowing the development of disabilities. However, early MS diagnosis is difficult because symptoms are intermittent and shared with other diseases. Thus most previous works have focused on uncovering the risk factors associated with MS and predicting the progression of disease after a diagnosis rather than disease prediction. This paper investigates the use of data available in electronic medical records (EMRs) to create a risk prediction model; thereby helping clinicians perform the difficult task of diagnosing an MS patient. Our results demonstrate that even given a limited time window of patient data, one can achieve reasonable classification with an area under the receiver operating characteristic curve of 0.724. By restricting our features to common EMR components, the developed models also generalize to other healthcare systems

The Brazilian Air Force Uniform Distribution Process: Using Lean Thinking, Statistical Process Control and Theory of Constraints to Address Improvement Opportunities

In the Brazilian Air Force, all enlisted Soldiers and Corporals (corresponding to USAF ranks from Airman Basic to Senior Airman) are entitled to receive their uniforms from their assigned, respective organizations, free of charge. An important portion of the uniform s distribution process is accomplished by the Brazilian Air Force Intendancy Central Depot (ICD). This organization carries the uniform inventory and performs the selection and distribution activities, following the guidance of the Sub-directorate of Supply. ICD process performance data for the peak distribution seasons of 2012 and 2013 was collected and analyzed. Several indications of inefficiencies became apparent, exemplified by a high number of late and partially fulfilled orders. This study applied fundamental principles of Lean Thinking, Statistical Process Control and the Theory of Constraints to identify potential areas for process improvement. Through statistical analyses, a simulation effort and capacity analysis, the negative impacts of variability throughout the process were assessed and several types of waste were recognized. Potential solutions to address the problems identified were suggested, as well as areas for further researc