Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

BACKGROUND: The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming. OBJECTIVE: To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES-based eCRF application could improve the accuracy and efficiency of the data entry process. METHODS: We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES-supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia. RESULTS: For the congenital heart disease condition, the NLP-MIES-supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES-supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001). CONCLUSIONS: Our system could improve both the accuracy and efficiency of the data entry process.

[Clinical application features of Fufang Kushen injection in treating malignant esophageal tumor： real world study based on hospital information system].

To analyze the clinical application features of Fufang Kushen injection in treating malignant esophageal tumors in the real world by using hospital information system database, and provide reference for clinical application of Fufang Kushen injection. The electronic medical data from 2 550 patients with malignant esophageal tumors using Fufang Kushen injection from 22 large-scale hospitals nationwide were extracted based on the hospital information system (HIS) established by Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences. Then the descriptive analysis based on frequency and rate was conducted for general characteristics, diagnostic characteristics, characteristics of dosage and medication information, characteristics of drug combination, and discharge outcome characteristics. The results showed that the average age of patients treated with Fufang Kushen injection for malignant esophageal tumors was 62.3 years old; more males than females; admitted to hospital mainly in department of cardiology, oncology and digestology first. The total efficiency was 47.15% based on the discharge outcome characteristics; the most common dosage was 10-20 mL for single use; the course of treatment was mainly 4-7 d; and the common drugs in drug combinations included dexamethasone, tropisetron injection, thymosin injection, compound amino acid injection, pantoprazole sodium injection, fluorouracil, et al. The characteristics of the crowd using Fufang Kushen injection to treat the malignant esophageal tumors were clear and in line with the general rule of malignant esophageal tumors; its clinical dosage and scope of treatment for malignant esophageal tumors in the real world were basically consistent with the specification; and the types of clinical drug use combinations were more extensive.

The influence of a specific ophthalmological electronic health record on ICD-10 coding.

BACKGROUND: A specific Electronic Health Record (EHR) for ophthalmology was introduced in an academic center in Germany. As diagnoses coding corresponding to the International Classification of Diseases Version 10 (ICD-10) is mandatory for billing reasons in Germany, we analyzed whether a change occurred in the diversity and number of diagnoses after the EHR introduction. The number of patients was also analyzed. Proper diagnoses coding is of the utmost importance for further data analysis or billing. METHODS: Graphical User Interfaces (GUIs) were created by using Advanced Business Application Programming language in EHR "i.s.h.med." Development of an EHR was conducted in close collaboration between physicians and software engineers. ICD-10 coding was implemented by using a "hit list" and a search engine for diagnoses. An observational analysis of a 6-month period prior to and after the introduction of an ophthalmological specific EHR was conducted by investigating the diversity and number of diagnoses in various ophthalmological disease categories and the number of patient consultations. RESULTS: During the introduction of a specific ophthalmological EHR, we observed a significant increase in the emergency department cases (323.9 vs. 359.9 cases per week), possibly related to documentation requirements. The number of scheduled outpatients didn't change significantly (355.12 vs. 360.24 cases per week). The variety of diagnoses also changed: on average, 156.2 different diagnoses were made per week throughout our hospital before the EHR launch, compared to 186.8 different diagnoses per week thereafter (p < 0.05). Additionally, a significantly higher number of diagnoses per case and per week were observed in both emergency and subspecialty outpatient clinics (1.15 vs. 1.22 and 1.10 vs. 1.47, respectively). CONCLUSIONS: An optimized EHR was created for ophthalmological needs and for simplified ICD-10 coding. The implementation of digital patient recording increased the diversity of the diagnoses used per case as well as the number of diagnoses coded per case. A general limitation to date is the suboptimal precision of ICD-10 coding in ophthalmology. Correct coding is of utmost importance for future data analysis.