A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

BACKGROUND: Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. OBJECTIVE: The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. METHODS: NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. RESULTS: Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid\u27s user interface and a number of definitions, and added 4502 more definitions in CoDeMed. CONCLUSIONS: Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients

Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity

Frequency and Predictors of Self-Reported Hypoglycemia in Insulin-Treated Diabetes

AIMS: Hypoglycemia is a limiting factor for achieving stringent glycemic control in diabetes. This study analyzes the frequency and predictors of hypoglycemia in insulin-treated diabetes in an ambulatory setting. METHODS: A retrospective chart review was performed to study self-monitored blood glucose (SMBG) data for 3 months prior to a patient\u27s HbA1c test. RESULTS: Hypoglycemia occurred more frequently in type 1 than in type 2 diabetes; however, 19% of type 2 diabetes patients did experience at least one episode of severe hypoglycemia. For type 1 diabetes, hypoglycemia had a positive association with glycemic variability and duration of diabetes and a negative association with HbA1c and lowest blood glucose (BG). For type 2 diabetes, a positive association was noted with glycemic variability and a negative association with age and lowest BG. CONCLUSIONS: Delineating factors predisposing to hypoglycemia in type 2 diabetes is difficult. Lower HbA1c is a potential predictor of hypoglycemia in type 1 but not in type 2 diabetes. Longer duration of diabetes for type 1 and younger age for type 2 are associated with more hypoglycemia. Glycemic variability portends increased risk for hypoglycemia and should be a focus of further research

Reducing analytical variation between point-of-care and laboratory HbA1c testing

BACKGROUND: Point-of-care (POC) HbA1c testing allows for timely treatment changes, improved glycemic control, and patient and provider satisfaction. Substantial variation between POC and laboratory HbA1c results has been reported. At our university hospital diabetes clinic, we observed significant negative bias in HbA1c with the DCA Vantage (Siemens Healthcare Diagnostics, Tarrytown, NY, USA) compared with the Tosoh G8 HPLC laboratory analyzer (Tosoh Bioscience, San Francisco, CA, USA). This led us to systematically analyze the bias with the goal of recalibrating the DCA to minimize bias. METHODS: We analyzed 45 patient samples, with HbA1c ranging between 5% and 10.8%, concurrently on two DCA analyzers and on the Tosoh G8 machine. The bias for each sample was the difference between the value on the DCA and the Tosoh G8 analyzer. Based on regression equations derived from the data, a correction factor for each DCA analyzer was calculated. The analyzers were recalibrated and retested for bias. RESULTS: At baseline, the mean bias (range) was -0.5229 (+0.1 to -1.3) for Analyzer 1 and -0.5348 (0.0 to -1.6) for Analyzer 2. After recalibration, the mean bias (range) was 0.000 (+0.6 to -0.6) and 0.0003 (+0.5 to -0.5) for Analyzers 1 and 2, respectively, and the systematic negative bias seen prior to the calibration was almost eliminated. CONCLUSIONS: We recommend periodic recalibration of POC analyzers to eliminate systematic unidirectional bias and to harmonize results between the POC and central laboratory analyzers within a healthcare system. Calibration may need to be repeated with any change in the reagent lot. University School of Medicine

Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

BACKGROUND: The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity. METHODS: We did a random-order crossover study in volunteers at least 18 years old who had type 1 diabetes and lived within a 30 min drive of four sites in the USA. Participants were randomly assigned (1:1) in blocks of two using sequentially numbered sealed envelopes to glycaemic regulation with a bihormonal bionic pancreas or usual care (conventional or sensor-augmented insulin pump therapy) first, followed by the opposite intervention. Both study periods were 11 days in length, during which time participants continued all normal activities, including athletics and driving. The bionic pancreas was initialised with only the participant\u27s body mass. Autonomously adaptive dosing algorithms used data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. The coprimary outcomes were the mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration less than 3.3 mmol/L, analysed over days 2-11 in participants who completed both periods of the study. This trial is registered with ClinicalTrials.gov, number NCT02092220. FINDINGS: We randomly assigned 43 participants between May 6, 2014, and July 3, 2015, 39 of whom completed the study: 20 who were assigned to bionic pancreas first and 19 who were assigned to the comparator first. The mean CGM glucose concentration was 7.8 mmol/L (SD 0.6) in the bionic pancreas period versus 9.0 mmol/L (1.6) in the comparator period (difference 1.1 mmol/L, 95% CI 0.7-1.6; p \u3c 0.0001), and the mean time with CGM glucose concentration less than 3.3 mmol/L was 0.6% (0.6) in the bionic pancreas period versus 1.9% (1.7) in the comparator period (difference 1.3%, 95% CI 0.8-1.8; p \u3c 0.0001). The mean nausea score on the Visual Analogue Scale (score 0-10) was greater during the bionic pancreas period (0.52 [SD 0.83]) than in the comparator period (0.05 [0.17]; difference 0.47, 95% CI 0.21-0.73; p=0.0024). Body mass and laboratory parameters did not differ between periods. There were no serious or unexpected adverse events in the bionic pancreas period of the study. INTERPRETATION: Relative to conventional and sensor-augmented insulin pump therapy, the bihormonal bionic pancreas, initialised only with participant weight, was able to achieve superior glycaemic regulation without the need for carbohydrate counting. Larger and longer studies are needed to establish the long-term benefits and risks of automated glycaemic management with a bihormonal bionic pancreas. FUNDING: National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and National Center for Advancing Translational Sciences