scholarly journals Beyond Interoperability with The Single Patient Viewer: A Clinical Portal to Access Integrated Patient Records

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Rosemary Foster ◽  
Alexa Heekes ◽  
Hannah Hussey ◽  
Mariette Smith ◽  
Themba Mutemaringa ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Clinical Care ◽  
Data Access ◽  
Global Public Good ◽  
Single Patient ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

IntroductionIn the Western Cape Province of South Africa, a wealth of patient-level data is collected through many separate electronic systems, which share a unique health identifier. However, clinicians primarily access paper folders, which can be unreliable, difficult to locate and are at risk of loss. Patients frequently attend multiple facilities and their information may not be accessible across facilities, hampering continuity of care. Objectives and ApproachFacilitated by the unique health identifier, a provincial Health Information Exchange (HIE), harmonises patient level data from routine systems into a health information exchange daily. The Single Patient Viewer (SPV) has been developed as a prototype web-based electronic health record and data access portal. SPV integrates clinical data for a single patient both longitudinally and cross-sectionally, in tabular and graphical views, to assist clinicians in rapid information discovery. The application is designed as a web application that calls a multi-purpose API that facilitates interoperability with the HIE. ResultSPV is in an extended pilot phase with over 200 clinicians using the portal, either for clinical care provision, or for clinical audit activities. The application has evolved to include referral, follow-up (voice call and messaging) and reporting functionality. In the past 6 months, over 17,000 patients have been viewed with an average daily search of 150 patients. An anonymised user survey with 52 participants showed that users felt that SPV was enjoyable to use, easy to learn, innovative, and supportive and valuable to their work. Conclusion / ImplicationsSPV has been developed as a global public good project and will be made freely available once matured. A unique feature of the development of SPV is that clinicians were using it while it was being built, enabling constant clinical user feedback. SPV demonstrates the value of integrating health data for clinical viewing while interoperable systems mature.

OEN: Multi-center, international, real-world evidence studies performed using health records without data pooling—The use of a common data model and shared analytical methods.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13554-e13554
Author(s):  
Bethany Levick ◽  
Sue Cheeseman ◽  
Eun Ji Nam ◽  
Haewon Doh ◽  
Subin Lim ◽  
...  
Keyword(s):  
Real World ◽  
Data Model ◽  
Clinical Care ◽  
Common Data Model ◽  
Local Data ◽  
Large Hospital ◽  
Patient Level Data ◽  
Patient Level ◽  
Real World Evidence ◽  
Level Data

e13554 Background: The value of real-world evidence derived from the care of patients managed outside the context of clinical trials is well recognised. However, the ability to link data from multiple centres, especially those from different countries, is complicated by complex legal and information governance differences. The Oncology Evidence Network is a collaboration of large hospital centres, with strong clinical informatics capabilities in six countries in Europe and Asia working with the support of an industrial partner to provide high quality, real world data reflecting routine clinical care. We have developed an efficient workflow based on a study-specific common data model (CDM) clinically validated at each site and analysed with a single analysis script, which embeds a set of data quality rules. Local implementation allows each centre to generate analytical outputs aligned across the different sites without the need for any patient level data to leave the participating site. This approach has been designed and tested in Epithelial Ovarian Cancer (EOC) patients. Methods: A CDM was agreed using expert advisors from each centre. Clinical alignment was achieved through iterative assessment of clinical vignettes, to ensure common definitions of clinical assessment, prognosis, and treatment algorithms in EOC patients. A data guide detailing variable level derivations and validation rules, general data coding principles, and conversions/codes from international coding systems was developed. The analysis scripts were implemented as a bespoke package (OpenOvary) in R. The package includes functions to validate the data against the CDM, and generate a standard output including tables, numerical summaries and Kaplan-Meier analysis of progression and overall survival. Results: 2,925 patient records from 6 centres across 6 countries were included in the study with 27 key data items curated by each centre. Treatment data is available detailing relevant surgical procedures and their outcomes, and regimens of SACT throughout patients’ care from diagnosis to death. Data completeness was generally high for key data items, with missing data ranging from 0-16% for FIGO stage at diagnosis and 0-14% for tumour morphology. The CDM and R script will be made publicly available for other centres to adopt and facilitate analysis of their local data. Conclusions: This collaboration has brought together a substantial body of data describing the care and outcomes for EOC patients. A CDM and flexible shared analysis approach enabled unified analysis and reporting whilst avoiding the transfer of patient level data and its pooling into a common database. The process of clinical and data alignment has generated a replicable model for rapid extension to other study centres to join the EOC study, or application to other disease areas.

scholarly journals Balancing health privacy, health information exchange, and research in the context of the COVID-19 pandemic

2020 ◽  
Vol 27 (6) ◽  
pp. 963-966 ◽  
Author(s):  
Leslie Lenert ◽  
Brooke Yeager McSwain
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Systems Of Care ◽  
Clinical Care ◽  
The Novel ◽  
Remote Testing ◽  
Specimen Collection ◽  
Care Processes ◽  
Novel Coronavirus

Abstract The novel coronavirus disease 2019 infection poses serious challenges to the healthcare system that are being addressed through the creation of new unique and advanced systems of care with disjointed care processes (eg, telehealth screening, drive-through specimen collection, remote testing, telehealth management). However, our current regulations on the flows of information for clinical care and research are antiquated and often conflict at the state and federal levels. We discuss proposed changes to privacy regulations such as the Health Insurance Portability and Accountability Act designed to let health information seamlessly and frictionlessly flow among the health entities that need to collaborate on treatment of patients and, also, allow it to flow to researchers trying to understand how to limit its impacts.

scholarly journals Privacy Preserving RNA-Model Validation Across Laboratories

2021 ◽  
Author(s):  
Talal Ahmed ◽  
Mark A Carty ◽  
Stephane Wenric ◽  
Jonathan R Dry ◽  
Ameen Abdulla Salahudeen ◽  
...  
Keyword(s):  
Data Privacy ◽  
Domain Adaptation ◽  
Data Access ◽  
Training Data ◽  
Patient Privacy ◽  
Sequencing Data ◽  
Adaptation Algorithm ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Reproducibility of results obtained using RNA data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification. While current RNA correction algorithms may overcome these differences, they require access to patient-level data which carries inherent risk of loss of privacy. Here, we describe SpinAdapt, a novel unsupervised domain adaptation algorithm that enables the transfer of molecular models across laboratories without access to patient-level sequencing data thereby minimizing privacy risk. SpinAdapt computes data corrections via aggregate statistics of each dataset, rather than requiring full sample-level data access, thereby maintaining patient data privacy. Furthermore, decoupling the model from its training data allows the correction of new streaming prospective data, enabling model evaluation on validation cohorts. SpinAdapt outperforms current correction methods that require patient-level data access. We expect this novel correction paradigm to enhance research reproducibility, quality, and patient privacy.

scholarly journals Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260885
Author(s):  
Son Q. Duong ◽  
Le Zheng ◽  
Minjie Xia ◽  
Bo Jin ◽  
Modi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Health Information ◽  
Information Exchange ◽  
Validation Cohort ◽  
Health Information Exchange ◽  
Clinical Care ◽  
Preventative Care ◽  
Increased Risk ◽  
New Onset

Background New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE. Methods and results We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified. Conclusions Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

scholarly journals Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

2021 ◽  
Vol 09 (02) ◽  
pp. E233-E238
Author(s):  
Rajesh N. Keswani ◽  
Daniel Byrd ◽  
Florencia Garcia Vicente ◽  
J. Alex Heller ◽  
Matthew Klug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Medical Center ◽  
Academic Medical Center ◽  
Procedure Time ◽  
Image Capture ◽  
Common Procedure ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

scholarly journals Maturity assessment of Kenya’s health information system interoperability readiness

2021 ◽  
Vol 28 (1) ◽  
pp. e100241
Author(s):  
Job Nyangena ◽  
Rohini Rajgopal ◽  
Elizabeth Adhiambo Ombech ◽  
Enock Oloo ◽  
Humphrey Luchetu ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Working Group ◽  
Data Exchange ◽  
Enterprise Architecture ◽  
Health Information Exchange ◽  
Digital Health ◽  
Health Information System ◽  
Maturity Level ◽  
Technical Standards

BackgroundThe use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS).ObjectiveTo determine the maturity readiness of the interoperability capacity of Kenya’s HIS.MethodsWe used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative’s Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health’s Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology.ResultsMost domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms.ConclusionNone of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

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