Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients

Because hospital errors, such as mistakes in documentation, cause one sixth of the deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety for patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is the best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings.https://scholarscompass.vcu.edu/uresposters/1273/thumbnail.jp

Patient access to complex chronic disease records on the internet

Background: Access to medical records on the Internet has been reported to be acceptable and popular with patients, although most published evaluations have been of primary care or office-based practice. We tested the feasibility and acceptability of making unscreened results and data from a complex chronic disease pathway (renal medicine) available to patients over the Internet in a project involving more than half of renal units in the UK. Methods: Content and presentation of the Renal PatientView (RPV) system was developed with patient groups. It was designed to receive information from multiple local information systems and to require minimal extra work in units. After piloting in 4 centres in 2005 it was made available more widely. Opinions were sought from both patients who enrolled and from those who did not in a paper survey, and from staff in an electronic survey. Anonymous data on enrolments and usage were extracted from the webserver. Results: By mid 2011 over 17,000 patients from 47 of the 75 renal units in the UK had registered. Users had a wide age range (<10 to >90 yrs) but were younger and had more years of education than non-users. They were enthusiastic about the concept, found it easy to use, and 80% felt it gave them a better understanding of their disease. The most common reason for not enrolling was being unaware of the system. A minority of patients had security concerns, and these were reduced after enrolling. Staff responses were also strongly positive. They reported that it aided patient concordance and disease management, and increased the quality of consultations with a neutral effect on consultation length. Neither patient nor staff responses suggested that RPV led to an overall increase in patient anxiety or to an increased burden on renal units beyond the time required to enrol each patient. Conclusions: Patient Internet access to secondary care records concerning a complex chronic disease is feasible and popular, providing an increased sense of empowerment and understanding, with no serious identified negative consequences. Security concerns were present but rarely prevented participation. These are powerful reasons to make this type of access more widely available

How good are we at determining risk? Quantifying the accuracy of clinician determined risk for VTE prophylaxis

Objectives: Create and validate a simple tool for concurrent audits of risk stratification, compliance and documentation Evaluate accuracy of clinician risk stratification and prophylatic ordering practice compared with a standardized Caprini RAM across different assigned risk categories Provide recommendations for EPIC VTE Prophylaxis CDS Developmenthttps://jdc.jefferson.edu/patientsafetyposters/1050/thumbnail.jp

Should we Set a Formalized Discharge Instruction Education Standard?

Smart AIM: With a more formalized discharge instruction evaluation process for PGY-1s, discharge instructions for specific diagnoses will have less error in a year’s time.https://jdc.jefferson.edu/patientsafetyposters/1035/thumbnail.jp

Use of Standardized Patient Scenarios to Train Medical Assistants in an Ambulatory Rehabilitation Medicine Clinic

Objectives: To improve the efficiency of our outpatient Rehabilitation Medicine clinic without sacrificing high value/quality patient care. To clarify the responsibilities of the MA and identify areas of redundancy in the rooming process. To demonstrate the utility of in-situ simulation for MA training. To reduce the time it takes for MAs to complete all assigned tasks to 10 minutes or less per encounter in at least 50% of patient encounters within two months from the time of intervention. To potentially highlight other areas in which to improve clinic efficiency and overall patient satisfaction (e.g. front desk registration process, resident and attending physician encounters, clinic and exam room accessibility).https://jdc.jefferson.edu/patientsafetyposters/1051/thumbnail.jp

Does Physician Education of Alternative Therapies for Obstructive Sleep Apnea Improve Utilization?

Methods: We conducted a retrospective chart review of patients in the Jefferson Sleep Disorder Center (JSDC) consisting of 2 cohorts of patients. The first was a group of patients undergoing PSG in March, 2014 prior to institution of the UAS program. The second was a cohort of patients undergoing PSG in July 2016 after institution of the UAS program, positive initial results, readily available literature in the JSDC, and a physician in-service including details of the procedure and outcome, quality of life, and complication rate data. We collected demographic and PSG data. We then reviewed the electronic medical record and assessed the first and second followupat the JSDC for CPAP compliance data and referral for oral appliance, body positioning device, or surgical evaluation.https://jdc.jefferson.edu/patientsafetyposters/1058/thumbnail.jp

Addressing Barriers to Breast Cancer Screening: Where to Intervene to Increase Mammogram Completion Rates

Methods: Study sought to determine if an intervention would aid in increasing mammogram screening rates in the Jefferson Family Medicine Associates practice.https://jdc.jefferson.edu/patientsafetyposters/1061/thumbnail.jp

Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016