Patient-Initiated Data: Our Experience with Enabling Patients to Initiate Incorporation of Heart Rate Data into the Electronic Health Record

Abstract Background Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. Objective This study aimed to develop a protocol to address millions of unreviewed heart rates. Methods As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. Results We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. Conclusion We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology.

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An Investigation of the Physical and Electrical Properties of Knitted Electrodes When Subjected to Multi-Axial Compression and Abrasion

Proceedings ◽

10.3390/proceedings2021068002 ◽

2021 ◽

Vol 68 (1) ◽

pp. 2

Author(s):

Arash M. Shahidi ◽

Theodore Hughes-Riley ◽

Carlos Oliveira ◽

Tilak Dias

Keyword(s):

Heart Rate ◽

Electrical Properties ◽

Applied Load ◽

Pressure Sensors ◽

Electrical Performance ◽

Electronic Textiles ◽

Heart Rate Monitors ◽

Mechanical Loads ◽

And Performance ◽

Over Time

Knitted electrodes are a key component to many electronic textiles including sensing devices, such as pressure sensors and heart rate monitors; therefore, it is essential to assess the electrical performance of these knitted electrodes under different mechanical loads to understand their performance during use. The electrical properties of the electrodes could change while deforming, due to an applied load, which could occur in the uniaxial direction (while stretched) or multiaxial direction (while compressed). The properties and performance of the electrodes could also change over time when rubbed against another surface due to the frictional force and generated heat. This work investigates the behavior of a knitted electrode under different loading conditions and after multiple abrasion cycles.

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Clinical Research Informatics: Contributions from 2017

Yearbook of Medical Informatics ◽

10.1055/s-0038-1641220 ◽

2018 ◽

Vol 27 (01) ◽

pp. 177-183 ◽

Cited By ~ 1

Author(s):

Christel Daniel ◽

Dipak Kalra ◽

Keyword(s):

Electronic Health Records ◽

Clinical Research ◽

Association Studies ◽

Bias Reduction ◽

Lessons Learned ◽

Editorial Team ◽

Private Industry ◽

Health Records ◽

Clinical Research Informatics ◽

Electronic Health

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2017. Method: A bibliographic search using a combination of MeSH descriptors and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 741 returned papers published in 2017 in the various areas of CRI, the full review process selected five best papers. The first best paper reports on the implementation of consent management considering patient preferences for the use of de-identified data of electronic health records for research. The second best paper describes an approach using natural language processing to extract symptoms of severe mental illness from clinical text. The authors of the third best paper describe the challenges and lessons learned when leveraging the EHR4CR platform to support patient inclusion in academic studies in the context of an important collaboration between private industry and public health institutions. The fourth best paper describes a method and an interactive tool for case-crossover analyses of electronic medical records for patient safety. The last best paper proposes a new method for bias reduction in association studies using electronic health records data. Conclusions: Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.

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Learning longitudinal patterns and identifying subtypes of pediatric Crohn disease treated with infliximab via trajectory cluster analysis of electronic health records

10.1101/2021.04.14.21255354 ◽

2021 ◽

Author(s):

Andrew Chen ◽

Ronen Stein ◽

Robert N. Baldassano ◽

Jing Huang

Keyword(s):

Electronic Health Records ◽

Disease Activity ◽

Crohn Disease ◽

Cross Sectional ◽

Health Records ◽

Disease Evolution ◽

Current Classification ◽

Electronic Health ◽

Over Time

ABSTRACTBackgroundThe current classification of pediatric CD is mainly based on cross-sectional data. The objective of this study is to identify subgroups of pediatric CD through trajectory cluster analysis of disease activity using data from electronic health records.MethodsWe conducted a retrospective study of pediatric CD patients who had been treated with infliximab. The evolution of disease over time was described using trajectory analysis of longitudinal data of C-Reactive Protein (CRP). Patterns of disease evolution were extracted through functional principal components analysis and subgroups were identified based on those patterns using the Gaussian mixture model. We compared patient characteristics, a biomarker for disease activity, received treatments, and long-term surgical outcomes across subgroups.ResultsWe identified four subgroups of pediatric CD patients with differential relapse-and-remission risk profiles. They had significantly different disease phenotype (p < 0.001), CRP (p < 0.001) and calprotectin (p = 0.037) at diagnosis, with increasing percentage of inflammatory phenotype and declining CRP and fecal calprotectin levels from Subgroup 1 through 4. The risk of colorectal surgery within 10 years after diagnosis was significantly different between groups (p < 0.001). We did not find statistical significance in gender or age at diagnosis across subgroups, but the BMI z-score was slightly smaller in subgroup 1 (p =0.055).ConclusionsReadily available longitudinal data from electronic health records can be leveraged to provide a deeper characterization of pediatric Crohn disease. The identified subgroups captured novel forms of variation in pediatric Crohn disease that were not explained by baseline measurements and treatment information.SummaryThe current classification of pediatric Crohn disease mainly relies on cross-sectional data, e.g., the Paris classification. However, the phenotypic classification may evolve over time after diagnosis. Our study utilized longitudinal measures from the electronic health records and stratified pediatric Crohn disease patients with differential relapse-and-remission risk profiles based on patterns of disease evolution. We found trajectories of well-maintained low disease activity were associated with less severe disease at baseline, early initiation of infliximab treatment, and lower risk of surgery within 10 years of diagnosis, but the difference was not fully explained by phenotype at diagnosis.

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Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2019.103363 ◽

2020 ◽

Vol 102 ◽

pp. 103363 ◽

Cited By ~ 1

Author(s):

Anna Ostropolets ◽

Christian Reich ◽

Patrick Ryan ◽

Ning Shang ◽

George Hripcsak ◽

...

Keyword(s):

Electronic Health Records ◽

Clinical Data ◽

Claims Data ◽

Lessons Learned ◽

Health Records ◽

Electronic Health

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The OpenDeID corpus for patient de-identification

Scientific Reports ◽

10.1038/s41598-021-99554-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jitendra Jonnagaddala ◽

Aipeng Chen ◽

Sean Batongbacal ◽

Chandini Nekkantti

Keyword(s):

Electronic Health Records ◽

Health Information ◽

Sensitive Information ◽

Protected Health Information ◽

Patient Privacy ◽

Health Records ◽

Automatic Methods ◽

Electronic Health ◽

Pathology Reports ◽

Privacy And Confidentiality

AbstractFor research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.

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Lessons learned from linking to 2001 census in Scotland

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.115 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Chris Povey

Keyword(s):

Linkage Study ◽

Lessons Learned ◽

Lookup Table ◽

Data Sets ◽

Time Dimension ◽

Health Records ◽

Match Rate ◽

Applied Lessons ◽

History Of ◽

Over Time

ABSTRACT ObjectivesSHELS (Scottish Health and Ethnicity Linkage Study) linked Scotland's 2001 census to various hospital and death data sets with national coverage. Census ethnicity data were assigned to the study records to build a cohort of most of the Scottish population; included in the cohort were people with no health records. ApproachCreate a lookup table of a person's census index to the Scottish eHealth index, the CHI, equivalent of English new national health number. A modified versionof the eHealth administrative matching system was used to satisfy census confidentiality requirements. There were two linkages performed in 2004 and 2008. 2004 was a feasibility run; the 2008 applied lessons learned from the previous linkage and used much more completely indexed health records. ResultsThe first linkage produced match rate of 95% of 4.9 million 2001 census entries; the second 96%. Conclusions Lessons learned. Linking datasets using indexes is the most accurate and efficient way to produce study cohorts. Indices change over time; a methodology called 'reconciliation' was devised to retrospectively and continually adjust previously indexed (linked) records. How to Track members who migrate out of the cohort. A linkage resource called a residential events dataset (RESEVENT) was built for the 2008 linkage run; it holds merged history of linkage identifier fields by date from january 2000 to the present based on GP registrations. This introduces a time dimension to indexed linking.How to build RESEVENT like linkage resources; should they be census based? What should they contain? How to do daily national census and select controls for case/control cohorts from RESEVENT resource. How postcode changes over time can be handled (reconciled) - same address, different postcode, but no address present. Proposal for an index of national indices based on national administrative datasets starting with NHS number (new and old NHSCR) and NI number to make linking even more efficient - this is not a RESEVENT resource; this resource would mean data need be matched to index only once, all subsequent linkages would be deterministic links of reconciled indices.

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EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

GigaScience ◽

10.1093/gigascience/giaa079 ◽

2020 ◽

Vol 9 (8) ◽

Cited By ~ 2

Author(s):

Carlos Sáez ◽

Alba Gutiérrez-Sacristán ◽

Isaac Kohane ◽

Juan M García-Gómez ◽

Paul Avillach

Keyword(s):

Electronic Health Records ◽

R Package ◽

Reliable Data ◽

Data Reuse ◽

Statistical Distributions ◽

Temporal Data ◽

Data Set ◽

Health Records ◽

Electronic Health ◽

Over Time

Abstract Background Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. Results EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. Conclusions EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface. Availability: https://github.com/hms-dbmi/EHRtemporalVariability/ Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.html Online demo: http://ehrtemporalvariability.upv.es/

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The effect of the presence of heart-rate monitors on the behaviour of group housed sows

Proceedings of the British Society of Animal Science ◽

10.1017/s0308229600031561 ◽

1996 ◽

Vol 1996 ◽

pp. 193-193

Author(s):

P.J. Baynes ◽

K. Graham ◽

E.J. Hunter ◽

H.J. Guise ◽

R.H.C. Penny

Keyword(s):

Heart Rate ◽

Resting Heart Rate ◽

Heart Rate Monitor ◽

Heart Rate Monitors ◽

Heart Rates ◽

Group Behaviour

Heart rate has been successfully recorded in sheep, deer and pigs (Baldock and Sibly, (1986) Price, Sibly and Davies, (1993) Webster et al, 1995). This work has shown that resting heart rate can increase in stressful situations. Being able to record heart rates of group-housed sows would complement behavioural observations, if it could be shown that the presence of the monitor did not alter group behaviour. The aim of this study was to assess in a group of active sows, the effect of the presence of a heart rate monitor on behaviour.

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Utilizing an Electronic Health Record System to Improve Vaccination Coverage in Children

Applied Clinical Informatics ◽

10.4338/aci-2009-12-cr-0028 ◽

2010 ◽

Vol 01 (03) ◽

pp. 221-231 ◽

Cited By ~ 12

Author(s):

A. Oster ◽

G. H. Yeh ◽

J. Magno ◽

H. M. Paek ◽

L. Au

Keyword(s):

Quality Improvement ◽

Electronic Health Records ◽

Childhood Vaccination ◽

Ambulatory Setting ◽

Quality Improvement Initiative ◽

Local Health ◽

Childhood Immunization ◽

Health Records ◽

Bar Code ◽

Electronic Health

Summary Background: Electronic Health Records (EHR) are widely believed to improve quality of care and effectiveness of service delivery. Use of EHR to improve childhood immunization rates has not been fully explored in an ambulatory setting. Objective: To describe a pediatric practice’s use of Electronic Health Records (EHR) in improving childhood immunization. Methods: A multi-faceted EHR-based quality improvement initiative used electronic templates with pre-loaded immunization records, automatic diagnosis coding, and EHR alerts of missing or delayed vaccinations. An electronic patient tracking system was created to identify patients with missing vaccines. Barcode scanning technology was introduced to aid speed and accuracy of documentation of administered vaccines. Electronic reporting to a local health department immunization registry facilitated ordering of vaccines. Results: Immunization completion rates captured in monthly patient reports showed a rise in the percentage of children receiving the recommended series of vaccination (65% to 76%) (p<0.000). Bar-code technology reduced the time of immunization documentation (86 seconds to 26 seconds) (p<0.000). Use of barcode scanning showed increased accuracy of documentation of vaccine lot numbers (from 95% to 100%) (p<0.000). Conclusion: EHR-based quality improvement interventions were successfully implemented at a community health center. EHR systems have versatility in their ability to track patients in need of vaccines, identify patients who are delayed, facilitate ordering and coding of multiple vaccines and promote interdisciplinary communication among personnel involved in the vaccination process. EHR systems can be used to improve childhood vaccination rates.

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