Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study

Establishing an effective clinical data collecting tool for optimal evaluation of native and allograft renal biopsies

Introduction

Percutaneous kidney biopsy is the gold standard method to reach a precise diagnosis in most medical kidney diseases, which positively impacts patient care by personalizing the treatment. Accurate diagnosis in the pathology report for medical kidney diseases requires clinicopathological correlation, and clinical data is not always reachable to the nephropathologist. This study aimed to create a standardized, paperless requisition form compatible with medical renal biopsies.

Methods

An initial form was prepared for native and allograft renal biopsies according to the current classification of medical kidney diseases. We invited 33 nephropathologists working in Canadian healthcare institutions to answer survey questions about the need to include a particular aspect of clinical information. According to the responses, we modified the experimental form. Eighty nephrologists were asked to complete a clinical data-collecting form given out as PDF files. The time for completing the form and clinicians' satisfaction were assessed.

Results

The experimental form survey was answered by 20 out of 33 nephropathologists (61%) from 14 Canadian healthcare centers. The agreement rate on the questions was from 38.89% to 100.00% (average 83.33% and 77.14% for the native and the allograft section, respectively). Seventeen out of 80 nephrologists and their assistants (21%) responded by completing 22 PDF forms. The time required to finish a PDF form was 10.4 min on average. Nephrologists considered the form time-consuming and suggested making it more clinically relevant. Only seven nephrologists responded to the satisfaction survey; four (57%) were satisfied.

Conclusions

Medical information is critical in renal pathology diagnoses. A uniform paperless clinical data requisition form was evolved through an agreement by Canadian nephropathologists.

Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review

BACKGROUND

Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit.

OBJECTIVE

To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes.

METHODS

The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay.

RESULTS

A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data.

CONCLUSION

With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.

Leveraging electronic medical record functionality to capture adenoma detection rate

Measuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. We utilized the Epic EHR. With the help of an Epic analyst, using existing tools, we developed a method by which endoscopy staff could mark whether an adenoma was detected for a given colonoscopy. Using these responses and all colonoscopies performed by the endoscopist recorded in the EHR, ADR was calculated in a report and displayed to endoscopists within the EHR. One endoscopist piloted the tool, and results of the tool were validated against a manual chart review. Over the pilot period the endoscopist performed 145 colonoscopies, of which 78 had adenomas. The tool correctly identified 76/78 colonoscopies with an adenoma and 67/67 of colonoscopies with no adenomas (97.4% sensitivity, 100% specificity, 98% accuracy). There was no difference in ADR as determined by the tool compared to manual review (53.1% vs. 53.8%, p = 0.912). We successfully developed and pilot tested a tool to measure ADR using existing EHR functionality.

The Acceptability and Efficacy of Electronic Data Collection in a Hospital Neurodevelopmental Clinic: Pilot Questionnaire Study

Background

There is a growing need for cost-efficient and patient-centered approaches to support families in hospital- and community-based neurodevelopmental services. For such purposes, electronic data collection (EDC) may hold advantages over paper-based data collection. Such EDC approaches enable automated data collection for scoring and interpretation, saving time for clinicians and services and promoting more efficient service delivery.

Objective

This pilot study evaluated the efficacy of EDC for the Child Development Unit, a hospital-based diagnostic assessment clinic in the Sydney Children’s Hospital Network. Caregiver response rates and preference for EDC or paper-based methods were evaluated as well as the moderating role of demographic characteristics such as age, level of education, and ethnic background.

Methods

Families were sent either a paper-based questionnaire via post or an electronic mail link for completion before attending their first on-site clinic appointment for assessment. A total of 62 families were provided a paper version of the questionnaire, while 184 families were provided the online version of the same questionnaire.

Results

Completion rates of the questionnaire before the first appointment were significantly higher for EDC (164/184, 89.1%) in comparison to paper-based methods (24/62, 39%; P<.001). Within the EDC group, a vast majority of respondents indicated a preference for completing the questionnaire online (151/173, 87.3%), compared to paper completion (22/173, 12.7%; P<.001). Of the caregiver demographic characteristics, only the respondent’s level of education was associated with modality preference, such that those with a higher level of education reported a greater preference for EDC (P=.04).

Conclusions

These results show that EDC is feasible in hospital-based clinics and has the potential to offer substantial benefits in terms of centralized data collation, time and cost savings, efficiency of service, and resource allocation. The results of this study therefore support the continued use of electronic methods to improve family-centered care in clinical practices.

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Objectives

This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field.

Methods and Results

This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals.

Discussion

Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

Auditing service delivery in glaucoma clinics using visual field records: a feasibility study

Objective

This study aimed to demonstrate that large-scale visual field (VF) data can be extracted from electronic medical records (EMRs) and to assess the feasibility of calculating metrics from these data that could be used to audit aspects of service delivery of glaucoma care.

Method and analysis

Humphrey visual field analyser (HFA) data were extracted from Medisoft EMRs from five regionally different clinics in England in November 2015, resulting in 602 439 records from 73 994 people. Target patients were defined as people in glaucoma clinics with measurable and sustained VF loss in at least one eye (HFA mean deviation (MD) outside normal limits ≥2 VFs). Metrics for VF reliability, stage of VF loss at presentation, speed of MD loss, predicted loss of sight years (bilateral VF impairment) and frequency of VFs were calculated.

Results

One-third of people (34.8%) in the EMRs had measurable and repeatable VF loss and were subject to analyses (n=25 760 patients). Median (IQR) age and presenting MD in these patients were 71 (61, 78) years and −6 (–10, –4) dB, respectively. In 19 264 patients with >4 years follow-up, median (IQR) MD loss was −0.2 (−0.8, 0.3) dB/year and median (IQR) intervals between VF examinations was 11 (8, 16) months. Metrics predicting loss of sight years and reliability of examinations varied between centres (p<0.001).

Conclusion

This study illustrates the feasibility of assessing aspects of health service delivery in glaucoma clinics through analysis of VF databases. Proposed metrics could be useful for blindness prevention from glaucoma in secondary care centres.

The Dutch Parelsnoer Institute Cerebrovascular Disease Initiative: a retrospective study of the effects of integrating clinical care and research on costs and quality of care in patients with ischaemic stroke

INTRODUCTION

The Dutch Parelsnoer Institute (PSI) is a collaboration between all university medical centres in which clinical data, imaging and biomaterials are prospectively and uniformly collected for research purposes. The PSI has the ambition to integrate data collected in the context of clinical care with data collected primarily for research purposes. We aimed to evaluate the effects of such integrated registration on costs, efficiency and quality of care.

METHODS

We retrospectively included patients with cerebral ischaemia of the PSI Cerebrovascular Disease Consortium at two participating centres, one applying an integrated approach on registration of clinical and research data and another with a separate method of registration. We determined the effect of integrated registration on (1) costs and time efficiency using a comparative matched cohort study in 40 patients and (2) quality of the discharge letter in a retrospective cohort study of 400 patients.

RESULTS

A shorter registration time (mean difference of -4.6 min, SD 4.7, p=0.001) and a higher quality score of discharge letters (mean difference of 856 points, SD 40.8, p<0.001) was shown for integrated registration compared with separate registration. Integrated registration of data of 300 patients per year would save around €700 salary costs per year.

CONCLUSION

Integrated registration of clinical and research data in patients with cerebral ischaemia is associated with some decrease in salary costs, while at the same time, increased time efficiency and quality of the discharge letter are accomplished. Thus, we recommend integrated registration of clinical and research data in centres with high-volume registration only, due to the initial investments needed to adopt the registration software.

India and the United Kingdom-What big data health research can do for a country

INTRODUCTION

Big data and growth in telecommunications have increased the enormous promise of an informatics approach to health care. India and the United Kingdom are two countries facing these challenges of implementing learning health systems and big data health research.

ANALYSIS

At present, these opportunities are more likely to be exploited in the private sector or in public-private partnerships (eg, Public Health Foundation of India [PHFI]) than public sector ventures alone. In both India and the United Kingdom, the importance of health informatics (HIs), a relatively new discipline, is being recognised and there are national initiatives in academic and health sectors to fill gaps in big data health research. The challenges are in many ways greater in India but outweighed by three potential benefits in health-related scientific research: (a) increased productivity; (b) a learning health system with better use of data and better health outcomes; and (c) to fill workforce gaps in both research and practice.

CONCLUSIONS

Despite several system-level obstacles, in India, big data research in health care can improve the status quo, whether in terms of patient outcomes or scientific discovery. Collaboration between India and the United Kingdom in HI can result in mutual benefits to academic and health care delivery organisations in both countries and can serve as examples to other countries embracing the promises and the pitfalls of health care research in the digital era.