Editing Health informatics (section)

===Clinical Research Informatics===

Clinical research informatics (CRI) is a sub-field of health informatics that tries to improve the efficiency of [[clinical research]] by using informatics methods. Some of the problems tackled by CRI are: creation of [[data warehouse]]s of health care data that can be used for research, support of data collection in [[clinical trials]] by the use of [[electronic data capture]] systems, streamlining ethical approvals and renewals (in [[US]] the responsible entity is the local [[institutional review board]]), maintenance of repositories of past clinical trial data (de-identified). CRI is a fairly new branch of informatics and has met growing pains as any up and coming field does. Some issue CRI faces is the ability for the statisticians and the computer system architects to work with the clinical research staff in designing a system and lack of funding to support the development of a new system.

Researchers and the informatics team have a difficult time coordinating plans and ideas in order to design a system that is easy to use for the research team yet fits in the system requirements of the computer team. The lack of funding can be a hindrance to the development of the CRI.  Many organizations who are performing research are struggling to get financial support to conduct the research, much less invest that money in an informatics system that will not provide them any more income or improve the outcome of the research (Embi, 2009). Ability to integrate data from multiple [[clinical trials]] is an important part of clinical research informatics. Initiatives, such as [[PhenX Toolkit|PhenX]] and [[Patient-Reported Outcomes Measurement Information System]] triggered a general effort to improve secondary use of data collected in past human clinical trials. CDE initiatives, for example, try to allow clinical trial designers to adopt standardized research instruments ([[case report form|electronic case report forms]]).<ref name="pm">{{cite journal | vauthors = Huser V, Shmueli-Blumberg D | title = Data sharing platforms for de-identified data from human clinical trials | journal = Clinical Trials | volume = 15 | issue = 4 | pages = 413–423 | date = August 2018 | pmid = 29676586 | doi = 10.1177/1740774518769655 | s2cid = 4993178 }}</ref>

A parallel effort to standardizing how data is collected are initiatives that offer de-identified patient level clinical study data to be downloaded by researchers who wish to re-use this data. Examples of such platforms are Project Data Sphere,<ref>{{cite web | url=https://www.projectdatasphere.org | title= Share, Integrate & Analyze Cancer Research Data | work = Project Data Sphere }}</ref> [[dbGaP]], ImmPort<ref>{{cite web | url=https://immport.niaid.nih.gov/home |title = ImmPort Private Data | work = National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) | publisher = United States Health and Human Services (HHS) }}</ref> or Clinical Study Data Request.<ref>{{cite web | title = ClinicalStudyDataRequest.com | url = https://ClinicalStudyDataRequest.com | publisher = ideaPoint, Inc }}</ref> Informatics issues in data formats for sharing results (plain [[Comma-separated values|CSV]] files, [[FDA]] endorsed formats, such as [[CDISC]] Study Data Tabulation Model) are important challenges within the field of clinical research informatics. There are a number of activities within clinical research that CRI supports, including:
* More efficient and effective data collection and acquisition
* Improved recruitment into clinical trials
* Optimal protocol design and efficient management
* [[Patient recruitment]] and management
* Adverse event reporting
* Regulatory compliance
* Data storage, transfer,<ref name="odm">{{cite journal | vauthors = Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ | title = Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) | journal = Journal of Biomedical Informatics | volume = 57 | pages = 88–99 | date = October 2015 | pmid = 26188274 | pmc = 4714951 | doi = 10.1016/j.jbi.2015.06.023 }}</ref> processing and analysis
* Repositories of data from completed clinical trials (for secondary analyses)

[[Image:OMOP (IMEDS) Common Data Model (version 4).png|250px|thumb|right|Example IDR schema]]
One of the fundamental elements of biomedical and translation research is the use of integrated data repositories. A survey conducted in 2010 defined "integrated data repository" (IDR) as a data warehouse incorporating various sources of clinical data to support queries for a range of research-like functions.<ref name=pmid22437072>{{cite journal | vauthors = MacKenzie SL, Wyatt MC, Schuff R, Tenenbaum JD, Anderson N | title = Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey | journal = Journal of the American Medical Informatics Association | volume = 19 | issue = e1 | pages = e119–24 | date = June 2012 | pmid = 22437072 | pmc = 3392848 | doi = 10.1136/amiajnl-2011-000508 }}</ref> Integrated data repositories are complex systems developed to solve a variety of problems ranging from identity management, protection of confidentiality, semantic and syntactic comparability of data from different sources, and most importantly convenient and flexible query.<ref name=pmid24534444>{{cite journal | vauthors = Wade TD, Zelarney PT, Hum RC, McGee S, Batson DH | title = Using patient lists to add value to integrated data repositories | journal = Journal of Biomedical Informatics | volume = 52 | pages = 72–7 | date = December 2014 | pmid = 24534444 | pmc = 4134416 | doi = 10.1016/j.jbi.2014.02.010 }}</ref>

Development of the field of clinical informatics led to the creation of large data sets with [[electronic health record]] data integrated with other data (such as genomic data). Types of data repositories include operational data stores (ODSs), clinical data warehouses (CDWs), clinical data marts, and clinical registries.<ref name="Nadkarni, P. M 2016. Pp 173-185">{{cite book | vauthors = Nadkarni P |year=2016 |chapter=Clinical Data Repositories: Warehouses, Registries, and the Use of Standards |chapter-url={{Google books|aJbBCQAAQBAJ|page=173|plainurl=yes}} |doi=10.1016/B978-0-12-803130-8.00009-9 |pages=173–85 |title=Clinical Research Computing: A Practitioner's Handbook |publisher=Academic Press |isbn=978-0-12-803145-2 }}</ref> Operational data stores established for extracting, transferring and loading before creating warehouse or data marts.<ref name="Nadkarni, P. M 2016. Pp 173-185" /> Clinical registries repositories have long been in existence, but their contents are disease specific and sometimes considered archaic.<ref name="Nadkarni, P. M 2016. Pp 173-185" /> Clinical data stores and clinical data warehouses are considered fast and reliable. Though these large integrated repositories have impacted clinical research significantly, it still faces challenges and barriers. Following is a list of major patient data warehouses with broad scope (not disease- or [[medical specialty|specialty]]-specific), with variables including laboratory results, pharmacy, age, race, socioeconomic status, comorbidities and longitudinal changes:
{| class="wikitable"
|+ Major patient data warehouses with broad scope 
! Warehouse !! Sponsor !! Main location !! Extent !! Access
|-
| Epic '''Cosmos'''<ref name=Fayanju2025>{{cite journal |vauthors=Fayanju OM, Haut ER, Itani K |title=Practical Guide to Clinical Big Data Sources |journal=JAMA Surg |volume=160 |issue=3 |pages=344–346 |date=March 2025 |pmid=39775674 |doi=10.1001/jamasurg.2024.6006 |url=}}</ref> || [[Epic Systems]] || [[United States]] || 296<ref>{{cite web|title=Epic Cosmos|website=Epic Systems website|url=https://cosmos.epic.com/|accessdate=2025-04-13}}</ref> million patients || Free for participating organizations
|-
| '''PCORnet'''<ref name=Fayanju2025/> || [[Patient-Centered Outcomes Research Institute]] (PCORI) || United States || 140 million patients || Free for participating organizations
|-
| '''OLDW''' (OptumLabs Data Warehouse) || [[Optum]] || United States || 160<ref>{{cite web}url=https://data.ucsf.edu/research/oldw|website=University of California San Francisco|title=OptumLabs Data Warehouse (OLDW)|accessdate=2025-04-13}}</ref> million patients || For a fee, or for free through certain academic institutions<ref>{{cite web|url=https://www.medschool.umaryland.edu/cibr/core/optumlabs/?utm_source=chatgpt.com|title=Optum Labs|website=University of Maryland|accessdate=2025-04-13}}</ref>
|-
| '''EHDEN'''<ref>{{cite journal |vauthors=Voss EA, Blacketer C, van Sandijk S, Moinat M, Kallfelz M, van Speybroeck M, Prieto-Alhambra D, Schuemie MJ, Rijnbeek PR |title=European Health Data & Evidence Network-learnings from building out a standardized international health data network |journal=J Am Med Inform Assoc |volume=31 |issue=1 |pages=209–219 |date=December 2023 |pmid=37952118 |pmc=10746315 |doi=10.1093/jamia/ocad214 |url=}}</ref> (European Health Data Evidence Network) || Innovative Health Initiative of the [[European Union]] || [[Europe]] || 133 million patients || Free for discovery. May have fees for secondary use.<ref>{{cite web}url=https://www.european-health-data-space.com/European_Health_Data_Space_Article_42_%28Proposal_3.5.2022%29.html|title=Articles of the European Health Data Space (EHDS), Article 42, Fees|website=The European Health Data Space (EHDS)|accessdate=2025-04-13}}</ref>
|}

One big problem is the requirement for ethical approval by the institutional review board (IRB) for each research analysis meant for publication.<ref name="Huser, V. 2013 PMC">{{cite journal | vauthors = Huser V, Cimino JJ | title = Don't take your EHR to heaven, donate it to science: legal and research policies for EHR post mortem | journal = Journal of the American Medical Informatics Association | volume = 21 | issue = 1 | pages = 8–12 | year = 2014 | pmid = 23966483 | pmc = 3912713 | doi = 10.1136/amiajnl-2013-002061 }}</ref> Some research resources do not require IRB approval. For example, CDWs with data of deceased patients have been de-identified and IRB approval is not required for their usage.<ref name="Huser, V. 2013 PMC" /><ref name="pmid22437072" /><ref name="Nadkarni, P. M 2016. Pp 173-185" /><ref name="pmid24534444" /> Another challenge is [[data quality]]. Methods that adjust for bias (such as using propensity score matching methods) assume that a complete health record is captured. Tools that examine data quality (e.g., point to missing data) help in discovering data quality problems.<ref>{{cite journal | vauthors = Huser V, DeFalco FJ, Schuemie M, Ryan PB, Shang N, Velez M, Park RW, Boyce RD, Duke J, Khare R, Utidjian L, Bailey C | title = Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets | journal = eGEMs | volume = 4 | issue = 1 | page = 1239 | year = 2016 | pmid = 28154833 | pmc = 5226382 | doi = 10.13063/2327-9214.1239 }}</ref>