Browsing by Subject "Electronic Health Records"
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Item The approaching singularity in medicine: when computers exceed physician performance(2014-01-31) Amarasingham, RubenItem Assessing the Relationship Between Electronic Medical Record (EMR) Generated QTc Alerts and Cancer Patient Mortality(2020-05-01T05:00:00.000Z) Bleiberg, Benjamin Aaron; Khan, Saad A.; Gerber, David E.; Terauchi, StephanieBACKGROUND: EMR generated drug associated QTc alerts are generated frequently in cancer patient populations and in the last few years their annual frequency has outgrown the number of unique patient visits at our institution. Anecdotally, they are largely ignored by providers, contributing to provider cognitive burnout and alert fatigue. While they may be considered nuisance alerts, the EMR collects rich data on the circumstances underlying the alert, the patients impacted, and their outcomes. By querying the EMR, we aimed to risk stratify patients by cancer site and demographic factors and provide meaning to these alerts allowing providers to incorporate the information we have provided in clinical decision making regarding the care of cancer patients. Our project is the first large-scale analysis of EMR generated QTc prolongation alerts at a tertiary referral center in the United States. OBJECTIVE: Acute mortality of cancer patients varies significantly by cancer site and demographic factors following EMR generated drug associated QTc interval prolongation alerts. METHODS: UT-Southwestern's EMR was queried to identify all patients between 10/04/2005 (the date of the 1st recorded alert) and 08/13/2019 over 18-years of age with a diagnosis of cancer and EMR generated drug associated QTc interval prolongation alerts yielding a sample of 19,223 patients. We collected the alert triggering medications, patient demographics, cancer site, and mortality data to identify the time between a patient's 1st alert and recorded death date. Rates of death by age, ethnicity, gender, race, number of alerts, and primary cancer site were identified in the following intervals: within 10 days, 11-180 days, and 181-365 days. Kaplan-Meier Overall Survival Analysis with Cox regression and multinomial multivariable analysis controlling for age, race, ethnicity, and gender, and median survival data analytic methods were used to identify if there were statistically significant differences in mortality at the pre-specified time points based on the above listed variables. Head and neck cancer patients were used as our reference group for comparison when analyzing mortality by primary cancer site. This group was chosen as their rates of within 10-day mortality closely aligned with those predicted by the null hypothesis that there would be no difference in mortality rates within 10-days across primary cancer sites used in our preliminary chi-squared goodness of fit model. Additionally, for patients with EKG's recorded within 10 days of their 1st alert, QTc intervals utilizing Bazett's correction algorithm were collected and the recorded interval of patients with deaths within 10 days was compared to those who were alive after 10 days, with participants separated by gender. RESULTS: Analysis of mortality from a patient's 1st QTc alert demonstrated statistically significant (p<0.05) higher risk of ≤10-day mortality in: patients with increasing number of QTc alerts, particularly patients with 6-10, HR=1.67 or >11 HR=1.88 alerts. Additionally, increased ≤10-day mortality was demonstrated in cancer patients with male gender, (female patients demonstrated a HR=0.83, compared to a male reference group), African American, HR=1.26, or other/unknown race, HR=1.29, and age >70, hazard ratio (HR)=2.32. Chi-squared goodness of fit testing identified head and neck cancer patients had a ratio of expected (by chance) to observed mortality of 0.98 and given the close concordance with our null model, were used as our reference group in multivariable analysis. Compared to a head and neck cancer patient baseline, significantly increased ≤10-day mortality was seen in: GI, HR=2.16; lung, HR=1.94; blood, HR=1.46; soft tissue, HR=2.26; and female genital, HR=1.55 cancers. Male genital, HR=0.39; breast, HR=0.57; and endocrine, HR=0.48 cancers had significantly decreased ≤10-day mortality. Of patients with EKG's, male patients who died ≤10-days of their 1st alert had significantly longer QTc intervals than males who survived to day 11 (469 vs 450 milliseconds, p<0.0001). In patients with any cancer and a QTC alert, 0.01 (male genital)-2.50% (GI) died ≤10 days of their 1st alert. The majority of deaths recorded <1 year after a patient's 1st alert occurred between 11-180 days during which 2.93 (male genital)-23.8% (GI) of the total sample with that primary cancer site diagnosis died. 63.2 (GI)-95.9% (endocrine) of cancer patients were alive >1 year after their 1st alert. CONCLUSION: Our research supports the anecdotal suggestion that very few patients die within 10 days of their initial QTc alert, suggesting that in many cases they function as distractions, especially in male genital, breast, and endocrine cancer patients and females or individuals <50 years of age. However, they may also identify patients at imminent risk of death, particularly those with lung, soft tissue, GI, blood, and female genital cancers, or males, African Americans, and individuals >70 years of age. Further, our analysis shows that QTc alerts may be a negative prognostic factor as the patients with more alerts (>5) have greater ≤10-day mortality rates. Additionally, of the patients who die within 365 days of their first alert the vast majority across cancer sites die between 11-180 days.Item Automated Quantification of Image-Derived Phenotypes and Integration with the Electronic Health Record at Scale in an Academic Biobank(2020-05-01T05:00:00.000Z) MacLean, Matthew Timothy; Hill, Joseph A.; de Lemos, James; Rader, DanielBACKGROUND: Radiographic images obtained during clinical care can yield a tremendous number of quantitative traits that facilitate translational research. Both liver fat and abdominal adipose mass are examples of quantitative traits that are highly relevant to human health and disease and can be quantitated from medical images such as CT scans. The Penn Medicine Biobank has generated genomic and biomarker data from >50,000 participants with consent to access EHR data. Among these patients, there are greater than 160,000 CT scans, representing over 19,000 patients from which these quantitative traits could be derived. However, manual review of imaging data is time-consuming, costly, and can produce variable results. OBJECTIVE: The goal of this research was to develop a fully automated pipeline to quantify hepatic fat and abdominal adipose mass from CT scans that could be run at scale on patients within the Penn Medicine Biobank. METHODS: We developed a fully automated image filtering and analysis pipeline to analyze CT imaging data. Deep learning networks were trained to identify the presence of IV contrast, delineate the borders of the liver and spleen, and detect visceral and subcutaneous fat. To identify CT studies, we queried our biobank of 52,441 patients for all non-contrast chest, abdomen, and all abdomen/pelvis scans and identified 161,748 CT scans from 19,624 patients. All scans were processed in our deep-learning pipeline in less than 96 hours using parallel processing with cloud-computing resources. From the imaging data, we extracted 12 different image-derived phenotypes that were used in association studies with the electronic health record (N>13000). We also performed genetic association studies (N>5000) on our CT-derived measure of liver fat (LF). Liver fat was defined as the difference in attenuation between the spleen and the liver. Receiver operator characteristic analysis of the liver fat metric was conducted by utilizing 135 patients who had both a CT scan as well as a liver biopsy. Finally, we performed principal component analysis to explore the interrelatedness of the image derived metrics in the context of the phenome. RESULTS: Each component of the algorithm was individually validated for accuracy. The first deep learning network (CNN1) functioned to identify IV contrast, and on a testing set of 400 (half with IV contrast), CNN1 classified 399 scans correctly. CNN2 was tasked with identifying the superior and inferior borders of the abdominal cavity and on a testing set of 100 scans was on average within one slice of the superior (1.01±1.11) and inferior (0.70±0.64) borders. Performance of CNN3, which was tasked with labeling liver, spleen, visceral and subcutaneous fat was assessed by computing region-of-interest area overlap ratios (Dice coefficients). Dice coefficients for a validation set of randomly selected CT scans showed high values for liver (0.95±0.02, n=20), spleen (0.92±0.07, n=20), abdominal compartment (0.98±0.01, n=10), subcutaneous fat (1.00±0.00, n=10), and visceral fat (0.99±0.01, n=10). After extracting the liver fat (LF) metric for all patients, LF had a mean of -6.4 ± 9.1 Hounsfield units. Association studies with billing codes in the electronic health record yielded many known associations for LF including with chronic liver disease, diabetes mellitus, obesity, and hypertension. A genetic association study showed significant associations with variants located in PNPLA3 and TM6SF2. ROC analysis using pathology data yielded an AUC value of 0.81 with a balanced cutoff value of -6 HU for LF. CONCLUSION: This paper presents a fully automated AI-based algorithm for the quantification of traits from medical imaging. This high-throughput algorithm was applied to over 160,000 scans to quantify 12 different traits and the results were integrated with phenome, genome, and pathology data in the Penn Medicine Biobank. This process is scalable, fast, and fault-tolerant and can power translational studies when integrated with clinical and genetic data.Item Health Information technology: has its adoption been worth it?(2016-01-29) Kazi, SalahuddinItem Improving Physician Behavior with an Obstetric Dashboard(2018-03-29) Xiong, Katherine Brenda; Reed, W. Gary; Horsager-Boehrer, Robyn; Phelps, EleanorOVERVIEW: A major complication of vaginal births is severe perineal laceration, and it is now an obstetric quality measure (AHRQ and The Joint Commission). One major risk factor of anal sphincter lacerations is episiotomy. National quality benchmarks recommend restricted use of episiotomy (in the absence of an indication like shoulder dystocia), with a recommended benchmark rate of less than 5.0% (Leapfrog) to reduce the occurrence of severe anal sphincter injuries. AIM STATEMENT: The aim of the primary phase was to reduce the episiotomy utilization by individual providers outside of the national benchmark by 10% and reduce the institutional rate by at least 25.0% in 6 months. The aim of the second phase was to reduce the frequency of severe perineal lacerations by 25% at CUH in 6 months. MEASURES OF SUCCESS: Incidence rate of episiotomy utilization by specific providers in spontaneous vaginal deliveries without shoulder dystocia and the incidence rate of severe perineal laceration in spontaneous vaginal deliveries without shoulder dystocia. INTERVENTIONS: In the primary phase, we instituted scheduled notifications of providers' episiotomy utilization rates using a physician dashboard. For our second project, heat pack application in the late first stage of labor was instituted. RESULTS: Following dashboard implementation, there was significant reduction in the institutional rate of episiotomy (9.0% pre-intervention vs. 2.7% post-intervention, p<0.001). However, no significant reduction in the frequency of severe perineal lacerations was observed (2.42% pre-intervention vs. 1.14% post-intervention, p=0.08). In the second study, we found the baseline incidence rate of severe perineal laceration to be 3.06% with no significant change in the incidence rate following initiation of our heat pack intervention (3.47% in the last quarter, p= 0.20). CONCLUSIONS AND NEXT STEPS: When variation in physician performance exists, utilization of a physician dashboard comparing individual provider behavior to peers can result in a significant improvement in provider and institutional performance on specific metrics.Item InBasket burden: what physicians and health systems can do to address the "elephant in the room"(2023-10-20) Anshasi, AhmadItem Investigation of Practice Facilitator Workflows for Enrollment Enhancement in ICD-Pieces Study(2018-03-22) Sakai, Mark; Reed, W. Gary; Vazquez, Miguel A.; Oliver, GeorgeBACKGROUND: Care for patients with multi-morbidities is challenging and often suboptimal. Earlier detection of patients with coexisting Chronic Kidney Disease (CKD), diabetes and hypertension served by our health care systems will allow us to institute appropriate care for the right patient at the right time with the right intervention thereby providing the greatest benefit. Implementation of interventions to treat CKD, diabetes, and hypertension and to treat associated conditions should reduce cardiovascular mortality and morbidity, improve clinical status, and reduce hospitalization and costs. A collaborative model approach to care for patients with multiple chronic conditions using the unique and novel technology platform provided by Pieces (Parkland intelligent e-coordination and evaluation system) is being investigated via pragmatic clinical trial. OBJECTIVE: The main hypothesis is that patients with CKD, hypertension and diabetes who receive care with a collaborative model of primary care-subspecialty care enhanced by novel information technology (Pieces) will have fewer hospitalizations, readmissions, CV events and deaths than patients receiving standard medical care. METHODS: The study employs a prospective stratified cluster randomization design involving four healthcare systems which are the stratum: Parkland Healthcare Systems, Texas Health Resources (THR), North Texas Veterans Affairs, and ProHealth Connecticut. Each of the four healthcare systems are unique in the populations that they serve, the electronic medical records that they utilize, and the qualifications of the practice facilitators that they employ. Practice facilitators at each of the participating sites received training on how to leverage the enhanced resources provided by Pieces. The practice facilitators are a crucial link that ensure consistent incorporation of Pieces technology into the care of patients selected for the intervention group of the study. The four unique practice facilitator workflows were diagrammed and proofed for accuracy. Challenges in the process identified by the practice facilitator were also cataloged. Similarities and differences noted in the workflows allowed the identification of the highest yield areas for improvement. Comparison of each of the four unique workflows to the original, "generic" workflow as well as to each other helped identify challenges consistent across all of the systems as well as ones unique to each system. RESULTS: The major challenge identified by each practice facilitator was the accuracy of the generated confirmed and candidate patient lists that they have been receiving. This led to decreased patient enrollments and resulted in the practice facilitators performing a manual survey of each patient. The inaccuracy of the lists was an indictment of the patient selection algorithm and leads one to question if all candidate patients were being identified. Other challenges identified by every practice facilitator included initial resistance from PCPs, missed appointments, and obtaining labs prior to appointments. Individually, each practice facilitator identified challenges that were unique to their situation. These challenges included the inability to sign lab orders, high overall workloads for pharmacists, and the inability to determine if PCPs had taken note of protocol recommendations. CONCLUSION: Investigation and comparison of the practice facilitator workflows at each of the four healthcare systems aided in the identification of shortfalls and challenges that have hindered the patient enrollment process. These workflows will be useful in future pragmatic studies that utilize the EMR in the identification of a patient population. It is also generally instructive for studies that seek to utilize EMRs to identify patient populations. Despite the theoretical efficacy of informatics application in healthcare, there is still much progress to be made in this arena. Nevertheless, the study as a whole will be an important part of the growing collection of pragmatic trials due to their increased external validity compared to traditional explanatory trials. It will also ultimately be a valuable learning tool in the construct and execution of future pragmatic trials and hopefully demonstrate that a collaborative model of primary care-subspecialty care that leverages information technology can improve the quality of patient care.Item Love it? hate it? it's complicated: electronic medical record user experience(2020-02-14) Chu, LingItem Mapping of Malnutrition from EMR Data in Southern Haiti(2015-01-26) Coston, Alec; Jostin, Franklin; Mularoni, MichialBACKGROUND: In Haiti, the problem of malnutrition is especially severe, exacerbated by the 2010 earthquake and a brutal rainy season. Local mapping of malnutrition with geographical information systems (GIS) makes it possible to analyze underlying geospatial risk factors of a region. In Port Salut, Klinik Timoun Nou Yo (KTNY) runs a nutrition program for affected children, and needs a tool for tracking their patient population. OBJECTIVE: A GIS tool is needed that is flexible and easy for clinical staff to operate. The tool will allow visualization of the geographical distribution of patients treated for malnutrition at KTNY. This information will aid in the analysis of malnutrition hotspots and seasonal admission trends, allowing the clinic to anticipate patient load and prepare resources effectively. METHODS: The GIS tool, called "KTNY Tracker" is written in python as a plug-in for the free software QGIS. As a proof of concept, patient data from 2013 and 2014 was uploaded and processed by the software, in order to qualitatively compare the yearly change in patient burden of the relatively new clinic, as well as assess the absolute patient distribution. RESULTS: The maps output by the software show a noticeable increase in patient load as well as catchment area from 2013 to 2014. This is most likely due to increased awareness about the clinic and its growing reputation, as well as extended efforts of patient pickup from remote areas. The map of overall patient load from 2013 to 2014 showed an expected high density of patients along the coastline, as well as low-density zones that may correlate with mountainous terrain, a lack of population, or other factors. These preliminary finding could be analyzed to elicit its true cause. DISCUSSIONS: KTNY Tracker in combination with QGIS's native functionality will prove to be a useful tool in visualizing clinical data and requires minimal training and experience to operate. Maps generated by this tool can serve as a visual advocate for more funding and resources for the clinic's malnutrition program. In the future, functionality will be added to assess follow-up success and failure rates, among other statistical tools for quantifying the visual data. The software is still in development, but it has promising potential as a clinical aid and as a launch pad for further studies.Item Medical errors: "first, do no harm"(2010-08-20) Reed, William GaryItem Medication safety and the electronic health record(2011-08-05) Croft, Carol L.Item Reducing Preventable Readmissions for Patients with Diabetes on the Parkland Hospitalist Units(2019-04-01) Chang, Huan Ting; Reed, W. Gary; Gunasekaran, Uma; Meneghini, LuigiBACKGROUND: High rates of readmission are detrimental to both the patient and the hospital, and they are associated with decreased patient satisfaction, diminished quality of life, and substandard overall care. Diabetes remains one of the greatest risk factors for 30-day all-cause readmissions. LOCAL PROBLEM: Under the Affordable Care Act (ACA), the Centers for Medicare and Medicaid Services (CMS) established the Hospital Readmissions Reduction Program (HRRP), which penalizes hospitals for high readmission rates related to heart failure, COPD, acute myocardial infarction, pneumonia, and stroke. Because diabetes was not a disease scrutinized under the HRRP at the start of the project period, Parkland was not specifically focused on reducing readmissions for patients with diabetes. METHODS: This quality improvement project utilizes the DMAIC framework. The proper context and measures were defined, and baseline process and outcome measures were obtained. A quality gap analysis was completed, and an FMEA was used to identify the gaps that needed to be addressed. Outcome and process measures were analyzed using chi-squared analysis and segmented control charts, and the balancing measures were analyzed using a continuous control chart. INTERVENTIONS: The first intervention was a rearrangement of the EMR nursing flowsheet drop-down menu used to document inpatient diabetes education to highlight diabetes survival skills first. The second intervention was the creation of the Diabetes Hospital Education and Resource Officer (HERO) Program which provided self-selected nurse champions across each hospital unit to be leaders in diabetes patient-care. RESULTS: Nine months after both interventions, there was a significant decrease in the 30-day all-cause readmission rate from the Parkland hospitalist unit by 5%. The documentation rates for insulin administration and hypoglycemia or hyperglycemia education increased significantly five months after the first intervention, and nine months after both interventions when compared to the baseline. Correlation analysis showed that with education, there was a decrease in readmission rates, but the changes were not significant. All three balancing measures remained in control during the project period. CONCLUSIONS: Changes to the EMR can create an immediate impact while continuous improvements need to be sustained by a leadership program with human factors.Item Standardizing the Intra-Operative Handover Between Faculty Anesthesiologists Using an EMR-Based Tool(2018-03-29) Sheng, Jim Zhengji; Reed, W. Gary; Bryson, Trenton; Greilich, PhilipSHORT DESCRIPTION: The primary aim of this project is to improve faculty satisfaction with a newly implemented intra-operative handoff tool. The secondary aim is to increase the effectiveness of the intra-operative handoffs by creating a user-friendly electronic medical record (EMR)-based cognitive aid designed to improve the reliability of this process. BACKGROUND: Communication failures during intra-operative handoffs can lead to adverse events and poor patient outcomes [1]. Faculty anesthesiologists frequently perform intra-operative handoffs as a part of their patient care responsibilities. While handoffs have garnered international attention calling for standardization [2,3], there are currently few specific recommendations on how intra-operative handoff should be completed. Checklists in the electronic medical record (EMR) have been shown to be effective in improving relay and retention of critical patient information during intra-operative transfers of care [3]. However, the essential elements and qualities in an intra-operative handoff tool have not been explored. This project identified the attributes in an EMR-based intra-operative handoff tool that are critical to faculty anesthesiologists at UT Southwestern Medical Center (UTSW). METHODS: Faculty anesthesiologists were interviewed for thoughts and comments about the current intra-operative handoff tool implemented at UTSW. Qualitative interview responses were separated into unique comments and analyzed for common themes. Quantitative results on opinions about current process handoff process and tool were determined. Critical-to-quality elements for effective intra-operative handoff tool were extracted from interview responses. EVALUATION AND OUTCOMES: Faculty had mixed opinions about current intra-operative handoff process, and most were unsatisfied about current handoff tool. From one-on-one interviews to explore faculty opinion, a total of 80 unique comments were generated regarding the tool, and 4 main themes were identified: patient information, tool functionality, data organization, and implementation. A total of 17 subtopics were identified based on comments. 15 critical-to-quality in an intra-operative tool was identified. IMPACT AND LESSONS LEARNED: Detailed faculty opinion and feedback regarding current intra-operative handoff process and tool at our institution were collected. Key critical-to-quality elements for an effective intra-operative handoff tool were identified and a proposed tool was created based on feedback. Further work will focus on working with electronic medical record system to develop updated and "ideal" tool based on results of this study. REFERENCES: 1. Commission, J. & Others. Improving America's hospitals: The Joint Commission's annual report on quality and safety. The Joint Commission, Oakbrook Terrace (2007). 2. The Joint Commision. "Sentinel Event Alert 58:Inadequate Hand-off Communication." Jointcommission.org, 11 Sept. 2017, www.jointcommission.org/sentinel_event_alert_58_inadequate_handoff_communications/, Accessed March 8, 2018. 3. World Health Organization Collaborating Center for Patient Safety: Communication during Patient Handovers. Geneva, Switzerland, WHO Press; 2007. Available at: http://www.who.int/patientsafety/solutions/high5s/High5_overview.pdf 4. Agarwala, Aalok V., et al. "An Electronic Checklist Improves Transfer and Retention of Critical Information at Intraoperative Handoff of Care." Anesthesia & Analgesia, vol. 120, no. 1, 2015, pp. 96-104., doi:10.1213/ane.0000000000000506.Item [TEST] Synopsis in the Electronic Medical Record: Recapturing the Apheresis Patient Story(2017-05) Armendariz, Tomas; Chernesky, Shelli; Lin, Christina; De Simone, Nicole; Sarode, RaviThis is a test sample.Item [TEST] Synopsis in the Electronic Medical Record: Recapturing the Apheresis Patient Story(2017-05) Armendariz, Tomas; Chernesky, Shelli; Lin, Christina; De Simone, Nicole; Sarode, Ravi[ This is a test sample.] PURPOSE: To report the implementation of a concise and easily accessible “apheresis synopsis” report to improve team communication, overall efficiency, patient safety and comfort. With the birth of electronic medical records (EMR), the patient story has been lost. In the past, physicians would review nursing apheresis procedure flowsheets in the paper charts. However, in the EMR used at our institution (EPIC®), physicians did not have access to apheresis display screens due to differing configurations based on provider type. An employed solution was for nurses to write a procedure summary note including final machine data. This documentation was not standardized and did not provide sequential data from previous procedures to make patient specific treatment decisions. Historical and current data drives these decisions. Historical data, such as inlet flow rates, use of calcium to treat citrate reactions, or use of tissue plasminogen activator, drives future treatment. Current data, such as most recent hematocrit or vital signs, are used to adjust procedure run parameters. Further inefficiencies occurred due to having to toggle between tabs in the EMR, such as between prior procedure notes, labs and recent vital signs, to determine parameters for that day’s procedure. METHODS: Physicians, advanced practice providers, nurses and technologists collaborated on identifying data points needed to assess patients for their apheresis procedures. Nursing worked with the EMR team to develop a well-organized, comprehensive and easily accessible report. All this was developed into the “apheresis synopsis” display screen within the EMR. This ensures that all relevant parameters are automatically displayed at the end of a procedure for ease of viewing. It also allows physicians to view sequential apheresis procedure data to observe for any trends, allowing appropriate adjustments in the apheresis plan. RESULTS: We now use the synopsis screen to help coordinate and improve patient care in our clinic. The synopsis is a summarized report displayed as a running timeline of procedures, containing information regarding dates, types of apheresis procedures, information of pre- and post-procedure machine parameters, vital signs, types of access, and adverse reactions. If more detailed information is needed, clicking the hyperlinked date opens expanded documentation for that procedure (Image). Providers can now review multiple parameters from multiple procedures without having to open various EMR tabs or encounters, thus making for more efficient and individualized treatment decisions by trending data. CONCLUSION: The patient story has been recaptured by maximizing the potential of the EMR through use of a synopsis. Communication between nurses and physicians has improved with standardized information available about each procedure leading to less frustration and ultimately, safer care.Item Using the Electronic Medical Record to Ensure Compliance with Opioid Prescription Laws in Texas(2019-03-28) Bender, Christopher McLean; Reed, W. Gary; Kandil, Enas; Fish, JasonBACKGROUND: The American population currently finds itself in the midst of a prescription drug overdose epidemic. This crisis has been fueled by an overreliance on opioid medications for the treatment of chronic pain. The state of Texas medical board (TMB) enacted a law change that restricts and regulates the prescribing and dispensing of controlled substances with respect to patients experiencing chronic pain. LOCAL PROBLEM: At the onset of this project, the University of Texas Southwestern (UTSW) system had no comprehensive measures in place to ensure compliance with these rules, and the current state of compliance was unknown. METHODS: Three clinics were chosen for observation to help understand the process of opioid prescribing for chronic pain treatment and the steps necessary to comply with the new law. Multiple Plan, Do, Study, Act (PDSA) cycles were applied to the process of baseline data measurement culminating in a final estimate of 3.1% ± 0.4% of applicable patient records written by UTSW providers in compliance with the law. INTERVENTIONS: Tools in the electronic medical record system (EMR) for tracking the use of scheduled medications in the treatment of chronic pain as well as for ensuring compliance with the new law have been developed and are in the process of implementation at the clinics with the largest populations of opioid-prescribed chronic pain patients. RESULTS: A chronic opioid registry was created, containing about 200 patients. Data retrieval is in process to determine the current rate of compliance. CONCLUSION: This project has successfully created a registry of the patients at UTSW on chronic opioid therapy and built an EMR structure that will ensure that these patients are cared for in a fashion compliant with TMB laws.Item [UT Southwestern Medical Center News](2011-01-13) Rian, Russell