Big data or big risk: general practitioner, practice nurse and practice manager attitudes to providing de-identified patient health data from electronic medical records to researchers

2020 ◽  
Vol 26 (6) ◽  
pp. 466
Author(s):  
Timothy Monaghan ◽  
Jo-Anne Manski-Nankervis ◽  
Rachel Canaway
Keyword(s):  
Primary Care ◽  
Health Information ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Health Data ◽  
Positive Effects ◽  
Patient Consent ◽  
Patient Health ◽  
General Practices ◽  
Patient Health Information

Research utilising de-identified patient health information extracted from electronic medical records (EMRs) from general practices has steadily grown in recent years in response to calls to increase use of health data for research and other secondary purposes in Australia. Little is known about the views of key primary care personnel on this issue, which are important, as they may influence whether practices agree to provide EMR data for research. This exploratory qualitative study investigated the attitudes and beliefs of general practitioners (GPs), practice managers (PMs) and practice nurses (PNs) around sharing de-identified EMR patient health information with researchers. Semi-structured interviews were conducted with 11 participants (6 GPs, 3 PMs and 2 PNs) recruited via purposive sampling from general practices in Victoria, Australia. Transcripts were coded and thematically analysed. Participants were generally enthusiastic about research utilising de-identified health information extracted from EMRs for altruistic reasons, including: positive effects on primary care research, clinical practice and population health outcomes. Concerns raised included patient privacy and data breaches, third-party use of extracted data and patient consent. These findings can provide guidance to researchers and policymakers in designing and implementing projects involving de-identified health information extracted from EMRs.

scholarly journals Health information exchange policies of 11 diverse health systems and the associated impact on volume of exchange

2016 ◽  
Vol 24 (1) ◽  
pp. 113-122 ◽  
Author(s):  
N Lance Downing ◽  
Julia Adler-Milstein ◽  
Jonathan P Palma ◽  
Steven Lane ◽  
Matthew Eisenberg ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
External Information ◽  
Policy Decisions ◽  
Optimal Care ◽  
Specific Patient ◽  
Patient Consent ◽  
Patient Health ◽  
Patient Health Information

Background: Provider organizations increasingly have the ability to exchange patient health information electronically. Organizational health information exchange (HIE) policy decisions can impact the extent to which external information is readily available to providers, but this relationship has not been well studied. Objective: Our objective was to examine the relationship between electronic exchange of patient health information across organizations and organizational HIE policy decisions. We focused on 2 key decisions: whether to automatically search for information from other organizations and whether to require HIE-specific patient consent. Methods: We conducted a retrospective time series analysis of the effect of automatic querying and the patient consent requirement on the monthly volume of clinical summaries exchanged. We could not assess degree of use or usefulness of summaries, organizational decision-making processes, or generalizability to other vendors. Results: Between 2013 and 2015, clinical summary exchange volume increased by 1349% across 11 organizations. Nine of the 11 systems were set up to enable auto-querying, and auto-querying was associated with a significant increase in the monthly rate of exchange (P = .006 for change in trend). Seven of the 11 organizations did not require patient consent specifically for HIE, and these organizations experienced a greater increase in volume of exchange over time compared to organizations that required consent. Conclusions: Automatic querying and limited consent requirements are organizational HIE policy decisions that impact the volume of exchange, and ultimately the information available to providers to support optimal care. Future efforts to ensure effective HIE may need to explicitly address these factors.

scholarly journals Implementing Blockchain to Enhance Usability of Patient-Generated Data

2019 ◽  
Vol 63 (1) ◽  
pp. 1344-1348 ◽  
Author(s):  
Steffen Baumann ◽  
Richard Stone ◽  
Esra Abdelall ◽  
Varun Srikrishnan ◽  
Thomas Schnieders ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Medical Devices ◽  
Medical Records ◽  
Point Of Care ◽  
Health Data ◽  
Privacy And Security ◽  
Address Data ◽  
Medical Licensing ◽  
Patient Health ◽  
Data Usability

The adoption of blockchain shows a variety of benefits owing to an incorruptible digital ledger and a decentralized database. This has eliminated the need for a gatekeeper to oversee all associated transactions. Blockchain, the underlying technology behind Bitcoin and other crypto-currencies, has found use in many industries besides finance, such as healthcare, where it is used for verifying medical licensing and credentialing, for tracking medical equipment (or consumables) from production to usage, and in cases associated with high levels of privacy and security. Patient data is collected using a plethora of patient-generated data devices, such as Internet of Things (IoT)-enabled wearables, health trackers, and home use medical devices. As a result, the data is siloed amongst several applications and/or vendors’ proprietary solutions. Of all this data, only some of it is transmitted to Electronic Medical Records. This produces the risk that not all data collected will be reviewed at the point of care due to the abundance of data collected. This article explains the areas within healthcare where blockchain could address data usability challenges and analyzes new ways in which patient health data can be collected to address the increasing number of challenges associated with the amount of data being generated over time. It also describes the drivers behind this data collection trend, the associated challenges and the subsequent ramifications. It concludes with a review of previous studies on data usability challenges and the means by which blockchain can be used to overcome these challenges.

908-P: Predictive Model for Glycemic Level of People with Diabetes Mellitus Using Data Extracted from Electronic Medical Records in Primary Care

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 908-P
Author(s):  
SOSTENES MISTRO ◽  
THALITA V.O. AGUIAR ◽  
VANESSA V. CERQUEIRA ◽  
KELLE O. SILVA ◽  
JOSÉ A. LOUZADO ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Predictive Model ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Glycemic Level ◽  
Using Data

Electronic medical records and primary care quality: Evidence from Manitoba

2021 ◽  
Vol 30 (5) ◽  
pp. 1124-1138
Author(s):  
Elisabet Rodriguez Llorian ◽  
Gregory Mason
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Care Quality ◽  
Primary Care Quality ◽  
Quality Evidence
Sign in / Sign up

Export Citation Format

Share Document