Dataset Information

Assessment of the relationship between diabetes treatment intensification and quality measure performance using electronic medical records.

ABSTRACT: AIMS:Assess the relationship between timely treatment intensification and hemoglobin A1C (HbA1C) control quality-of-care performance measures, i.e., HbA1C levels, among patients with uncontrolled type 2 diabetes. MATERIALS AND METHODS:Electronic medical records and diabetes registry data from a large, accountable care organization (ACO) were used to isolate a sample of adult patients with type 2 diabetes who received at least one oral antidiabetes agent and had at least one HbA1C level measurement ?8.0% (64 mmol/mol; i.e., uncontrolled diabetes) between 7/1/2011 and 6/30/2015. Treatment intensification status was evaluated for each patient during a 120-day treatment intensification window following the index HbA1c measure. Two-level hierarchical generalized linear models, with patients aggregated at the physician level, were used to assess the association between treatment intensification and achieving HbA1C quality performance measures. RESULTS:547 patients met study selection criteria and 480 patients had at least one HbA1C test after the treatment intensification window and were used for the statistical analyses. About 40% of patients who had uncontrolled diabetes received treatment intensification during the 120-day window. Greater index HbA1C, greater patient body mass index, and fewer unique pre-index oral antidiabetes agents were significantly associated with greater likelihood of receiving timely treatment intensification. The odds of receiving treatment intensification were about 1.8 times higher (P = 0.0027) among patients with poor index HbA1C control (HbA1c level >9.0% [75 mmol/mol]) compared to other patients (index HbA1c 8.0% - 9.0%). Hispanic patients (compared to White patients) were significantly more likely to exhibit poor control after treatment intensification (odds ratio [OR] 2.91, P = 0.0304), underscoring the difficulty of controlling diabetes in this vulnerable group. In contrast, being male and being treated primarily by an internist (compared to primary treatment by a family medicine specialist) were both significantly associated with achieving superior control (HbA1c level <8.0%) after treatment intensification (OR 0.53 [P = 0.0165]; OR 0.41 [P = 0.0275], respectively). CONCLUSIONS:Timely treatment intensification was significantly associated with greater likelihood of patients achieving superior HbA1C control (<8.0%) and better HbA1C control quality performance for the practice. Even in an ACO with resources dedicated to diabetes control, it is incumbent upon clinicians to readily identify and open dialogues with patients who may benefit from closely supervised, individualized attention.

SUBMITTER: Arnold RJG

PROVIDER: S-EPMC5997332 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of the relationship between diabetes treatment intensification and quality measure performance using electronic medical records.

Arnold Renée J G RJG Yang Shuo S Gold Edward J EJ Farahbakhshian Sepehr S Sheehan John J JJ

PloS one 20180612 6

<h4>Aims</h4>Assess the relationship between timely treatment intensification and hemoglobin A1C (HbA1C) control quality-of-care performance measures, i.e., HbA1C levels, among patients with uncontrolled type 2 diabetes.<h4>Materials and methods</h4>Electronic medical records and diabetes registry data from a large, accountable care organization (ACO) were used to isolate a sample of adult patients with type 2 diabetes who received at least one oral antidiabetes agent and had at least one HbA1C ...[more]

PMID: 29894495

Similar Datasets

Project description:BackgroundDrug safety in children is a major concern; however, there is still a lack of methods for quantitatively measuring, let alone to improving, drug safety in children under different clinical conditions. To assess pediatric drug safety under different clinical conditions, a computational method based on Electronic Medical Record (EMR) datasets was proposed.MethodsIn this study, a computational method was designed to extract the significant drug-diagnosis associations (based on a Bonferroni-adjusted hypergeometric P-value <?0.05) among drug and diagnosis co-occurrence in EMR datasets. This allows for differences between pediatric and adult drug use to be compared based on different EMR datasets. The drug-diagnosis associations were further used to generate drug clusters under specific clinical conditions using unsupervised clustering. A 5-layer quantitative pediatric drug safety level was proposed based on the drug safety statement of the pediatric labeling of each drug. Therefore, the drug safety levels under different pediatric clinical conditions were calculated. Two EMR datasets from a 1900-bed children's hospital and a 2000-bed general hospital were used to test this method.ResultsThe comparison between the children's hospital and the general hospital showed unique features of pediatric drug use and identified the drug treatment gap between children and adults. In total, 591 drugs were used in the children's hospital; 18 drug clusters that were associated with certain clinical conditions were generated based on our method; and the quantitative drug safety levels of each drug cluster (under different clinical conditions) were calculated, analyzed, and visualized.ConclusionWith this method, quantitative drug safety levels under certain clinical conditions in pediatric patients can be evaluated and compared. If there are longitudinal data, improvements can also be measured. This method has the potential to be used in many population-level, health data-based drug safety studies.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

Assessment of the relationship between diabetes treatment intensification and quality measure performance using electronic medical records.

Publications

Assessment of the relationship between diabetes treatment intensification and quality measure performance using electronic medical records.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets