Health care analytics

Health care analytics is a term used to describe the healthcare analysis activities that can be undertaken as a result of data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs)), and patient behavior and sentiment data (patient behaviors and preferences, (retail purchases e.g. data captured in running stores).[1] Health care analytics is a growing industry in the United States, expected to grow to more than $18.7 billion by 2020. The industry focuses on the areas of clinical analysis, financial analysis, supply chain analysis, as well as, fraud and HR analysis. The current largest players in the market are medical consulting and medical software companies (IBM Corporation, SAS Institute, Inc. Optum, Inc, Truven Health Analytics Inc., Cerner Corporation, and McKesson Corporation)[1] who have the ability to extend their reach with already existing customers.

Health care analytics allows for the examination of patterns in various healthcare data in order to determine how clinical care can be improved while limiting excessive spending.

Citing consultant George Zachariah from Dynamics Research Corporation, Healthcare IT News noted several potential benefits from health care analytics: 1) “cut[ting] down administrative costs,” 2) “clinical decision support,” 3) “cut[ting] down on fraud and abuse,” 4) “better care coordination,” and 5) “improv[ing] patient wellness.”[2] A research article by Betty Jo Rochio further discussed how data analytics could be used to lower costs by “reduc[ing] variation in supplies, labor, and overhead.”[3]

Federal government role in health IT

The United States Department of Health and Human Services’ (HHS) Office of the National Coordinator for Health Information Technology (ONC) issued the Federal Health IT Strategic Plan 2015-2020.[4] The plan outlines the steps federal agencies will take to achieve widespread use of health information technology (health IT) and electronic health information to enhance the health IT infrastructure, to advance person-centered and self-managed health, to transform health care delivery and community health, and to foster research, scientific knowledge and innovation.[5] The plan is intended “to provide clarity in federal policies, programs, and actions and includes strategies to align program requirements, harmonize and simplify regulations, and aims to help health IT users to advance the learning health system to achieve better health.”[4]

The Strategic Plan includes several key initiatives employing multiple strategies to meet its goals. These include: (1) finalizing and implementing an interoperability roadmap; (2) protecting the privacy and security of health information; (3) identifying, prioritizing and advancing technical standards; (4) increasing user and market confidence in the safety and safe use of health IT; (5) advancing a national communication infrastructure; and (6) collaborating among all stakeholders.[4]

Challenges to address

Creating an interoperability roadmap

There are three fundamental challenges to be addressed: (1) variation in how standards are tested and implemented; (2) variation in how health IT stakeholders interpret and implement policies and legal requirements; and (3) reluctance of health IT stakeholders to share and collaborate in ways that might foster consumer engagement.[5]

The ONC is working to develop a policy advisory for health information exchange by 2017 that will define and outline basic expectations for trading partners around health information exchange, interoperability and the exchange of information.[5] Current federal and state law only prohibits certain kinds of information blocking in limited and narrow circumstances, for example, under the Health Insurance Portability and Accountability Act (HIPAA) or the Anti-Kickback statute.[5]

Protecting privacy and security

In addition to HIPAA, many states have their own privacy laws protecting an individual’s health information. State laws that are contrary to HIPAA are generally preempted by the federal requirements unless a specific exception applies. For example, if the state law relates to identifiable health information and provides greater privacy protections, then it is not preempted by HIPAA. Since privacy laws may vary from state-to-state, it may create confusion among health IT stakeholders and make it difficult to ensure privacy compliance.[5]

Establishing common technical standards

Use of common technical standards is necessary to move electronic health information seamlessly and securely. While some clinical record content, such as laboratory results and clinical measurements are easily standardized other content, such as provider notes may be more difficult to standardize. Methods need to be identified that allow for the standardization of provider notes and other traditionally “free form text” data.

The ONC HIT Certification Program[6] certifies that a system meets the technological capability, functionality and security requirements adopted by HHS. ONC will assess the program on an ongoing basis “to ensure it can address and reinforce health IT applications and requirements that support federal value-based and alternative payment models.”[4]

Increasing confidence in safety and safe use of health IT

Health care consumers, providers and organizations need to feel confident that the health IT products, systems or services they are using are not only secure, safe and useful but that they can switch between products, systems or services without loss of valuable information or undue financial burden. Implementation of the Federal Health IT Strategic Plan 2015-2020, along with the 2013 HHS Health IT Patient Safety Action and Surveillance Plan and 2012 Food and Drug Administration Safety and Innovation Act will attempt to address these concerns.[4]

Developing national communications structure

A national communications infrastructure is necessary to enable the sharing of electronic health information between stakeholders, including providers, individuals and national emergency first responders. It is also necessary for delivering telehealth services or using mobile health applications. “Expanded, secure, and affordable high-speed wireless and broadband services, choice, and spectrum availability will support electronic health information sharing and use, support the communication required for care delivery, and support the continuity of health care and public health services during disasters and public health emergencies.”[4]

Stakeholder collaboration

The federal government in its role as contributor, beneficiary and collaborator “aims to encourage private-sector innovators and entrepreneurs, as well as researchers, to use government and government-funded data to create useful applications, products, services, and features that help improve health and health care.” HHS receives funds from the Patient-Centered Outcomes Research Trust Fund to build data capacity for patient-centered outcomes research. It is estimated HHS will receive over $140 million for the period between 2011 and 2019. These funds will be used “to enable a comprehensive, interoperable, and sustainable data network infrastructure to collect, link, and analyze data from multiple sources to facilitate patient-centered outcomes research.”[4]

Legislation

Meaningful Use, the Patient Protection and Affordable Care Act (ACA) and the declining cost of data storage[7] results in health data being stored, shared, and used by multiple providers, insurance companies, and research institutions. Concerns exist about how organizations gather, store, share, and use personal information, including privacy and confidentiality concerns, as well as the concerns over the quality and accuracy of data collected. Expansion of existing regulation can ensure patient privacy and guard patient safety to balance access to data and the ethical impact of exposing that data.

Balancing Interests – innovation, privacy, and patient safety

Complete freedom to access to data may not provide the best protection for patient rights. Expansive limits on the collection of data may unnecessarily limit its' potential usefulness. In addition to data collection, there are concerns regarding risk of statistical errors,[8] erroneous conclusions or predictions,[2] and misuse of results.[9] Appropriate policies could support gains in process improvements, cost reductions, personalized medicine, and population health. Additionally, providing incentives to encourage appropriate use may address some concerns but could also inadvertently incentivize the misuse of data.[10] Lastly, creating standards for IT infrastructure may encourage data sharing and use, but those standards would need to be reevaluated on a regular, ongoing basis as the fast pace of technological innovation causes standards and best practices to become quickly outdated.

Potential areas to address through legislation

Limiting data collection

The needs of healthcare providers, government agencies, health plans, and researchers for quality data must be met to ensure adequate medical care and to make improvements to the healthcare system, while still ensuring the patients right to privacy. Data collection should be limited to necessity for medical care and by patient preference beyond that care. Such limits would protect patient privacy while minimizing infrastructure costs to house data. When possible, patients should be informed about what data is collected prior to engaging in medical services.[11]

Limiting data use

Expanding availability of big data increases the risk of statistical errors,[1] erroneous conclusions and predictions,[2] and misuse of results.[12] Evidence supports use of data for process improvements,[13][14][15] cost reductions,[16] personalized medicine,[17] and public health.[18] Innovative uses for individual health[17][19] can harm underserved populations.[20] Limiting use for denial and exclusion prevents use to determine eligibility for benefits or care and is harmonized with other U.S. anti-discrimination laws, such as Fair Credit Reporting Act, and is harmonized with anti-discrimination laws like the Civil Rights Act and the Genetic Information Nondiscrimination Act.

Providing incentives to encourage appropriate use

Increasing vertical integration in both public and private sector providers[21] has created massive databases of electronic health records.[22] The ACA has provided Medicare and Medicaid incentives to providers to adopt EHR's.[11] Large healthcare institutions also have internal motivation to apply healthcare analytics, largely for reducing costs by providing preventative care.[23] Policy could increase data use by incentivizing insurers and providers to increase population tracking, which improves outcomes.[10]

To enforce compliance with regulations, the government can use incentives similar to those under the ACA for Medicare and Medicaid to use electronic health records.[11]

Creating standards for the IT infrastructure

Inappropriate IT infrastructure likely limits healthcare analytics findings and their impact on clinical practice.[9] Establishing standards ensures IT infrastructure capable of housing big data balanced with addressing accessibility, ownership, and privacy.[23] New possibilities could be explored such as private clouds and “a virtual sandbox” consisting of filtered data authorized to the researchers accessing the sandbox.[9][24] Standards promote easier coordination in information collaboration between different medical and research organizations[9] resulting in significantly improving patient care by improving communication between providers and reducing duplicity and costs.

Minimum standards are necessary to balance privacy and accessibility.[9] Standardization helps improve patient care by facilitating research collaboration and easier communication between medical providers.[9] The research can yield preventive care concepts that can reduce patient caseload and avoid long-term medical costs.

References

  1. 1 2 3 Fan, Jianqing; Han, Fang; Liu, Han (2014-06-01). "Challenges of Big Data analysis". National Science Review. 1 (2): 293–314. doi:10.1093/nsr/nwt032. ISSN 2095-5138.
  2. 1 2 3 PhD, Austin B. Frakt, PhD, and Steven D. Pizer, (2016-02-16). "The Promise and Perils of Big Data in Healthcare". American Journal of Managed Care. 22 (February 2016 2).
  3. ↑ Rocchio, Betty Jo (2016-10-01). "Achieving Cost Reduction Through Data Analytics". AORN journal. 104 (4): 320–325. doi:10.1016/j.aorn.2016.07.010. ISSN 1878-0369. PMID 27692078.
  4. 1 2 3 4 5 6 7 "Federal Health IT Strategic Plan 2015-2020" (PDF). Retrieved 21 October 2016.
  5. 1 2 3 4 5 "2015 Update to Congress on the Adoption of Health Information Technology". dashboard.healthit.gov. Retrieved 2016-10-22.
  6. ↑ "ONC Health IT Certification Program | Policy Researchers & Implementers | HealthIT.gov". www.healthit.gov. Retrieved 2016-10-22.
  7. ↑ "The Cost of Data Storage and Management: Where Is It Headed in 2016?". The Data Center Journal. Retrieved 2016-10-22.
  8. ↑ Fan, Jianqing; Han, Fang; Liu, Han (2014-06-01). "Challenges of Big Data analysis". National Science Review. 1 (2): 293–314. doi:10.1093/nsr/nwt032. ISSN 2095-5138. PMC 4236847Freely accessible. PMID 25419469.
  9. 1 2 3 4 5 6 Roski, Joachim; Bo-Linn, George W.; Andrews, Timothy A. (2014-07-01). "Creating Value In Health Care Through Big Data: Opportunities And Policy Implications". Health Affairs. 33 (7): 1115–1122. doi:10.1377/hlthaff.2014.0147. ISSN 0278-2715. PMID 25006136.
  10. 1 2 "IBM The value of analytics in healthcare". www-935.ibm.com. 2015-12-10. Retrieved 2016-10-22.
  11. 1 2 3 Inc., Advanced Solutions International,. "Affordable Care Act Implementation and Information". www.amga.org. Retrieved 2016-10-22.
  12. ↑ "FTC Warns Against Use and Misuse of Big Data Analytics | Marketing Research Association". www.marketingresearch.org. Retrieved 2016-10-08.
  13. ↑ Khalifa, Mohamed (2016-01-01). "Utilizing Health Analytics in Improving Emergency Room Performance". Studies in Health Technology and Informatics. 225: 138–142. ISSN 0926-9630. PMID 27332178.
  14. ↑ Liu, Hongfang; Kaggal, Vinod; Elayavilli, Ravikumar Komandur; Mehrabi, Saeed; Pankratz, Joshua; Sohn, Sunghwan; Wang, Yanshan; Li, Dingcheng; Rastegar, Majid Mojarad (2016-06-23). "Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP". Biomedical Informatics Insights. 2016 (Suppl. 1). doi:10.4137/bii.s37977.
  15. ↑ Janke, Alexander T.; Overbeek, Daniel L.; Kocher, Keith E.; Levy, Phillip D. "Exploring the Potential of Predictive Analytics and Big Data in Emergency Care". Annals of Emergency Medicine. 67 (2): 227–236. doi:10.1016/j.annemergmed.2015.06.024.
  16. ↑ "IOS Press Ebooks - Exploring Lab Tests Over Utilization Patterns Using Health Analytics Methods". doi:10.3233/978-1-61499-664-4-190.
  17. 1 2 Maglaveras, Nicos; Kilintzis, Vassilis; Koutkias, Vassilis; Chouvarda, Ioanna (2016-01-01). "Integrated Care and Connected Health Approaches Leveraging Personalised Health through Big Data Analytics". Studies in Health Technology and Informatics. 224: 117–122. ISSN 0926-9630. PMID 27225565.
  18. ↑ Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David (2015-12-01). "Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors". Big Data. 3 (4): 277–287. doi:10.1089/big.2015.0020. ISSN 2167-6461.
  19. ↑ "FTC Report Provides Recommendations to Business on Growing Use of Big Data | Federal Trade Commission". www.ftc.gov. Retrieved 2016-10-08.
  20. ↑ "Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues (FTC Report) | Federal Trade Commission". www.ftc.gov. Retrieved 2016-10-08.
  21. ↑ "Health Care & Analytics - Analytics Magazine". Analytics Magazine. 2011-09-04. Retrieved 2016-10-22.
  22. ↑ "Keeping Up With Meaningful Use: Clinical Analytics Are Key". Health Catalyst. 2013-11-21. Retrieved 2016-10-22.
  23. 1 2 "3 ways big data is improving healthcare analytics". Healthcare IT News. 2015-07-17. Retrieved 2016-10-22.
  24. ↑ Marshall, Deborah A.; Burgos-Liz, Lina; Pasupathy, Kalyan S.; Padula, William V.; IJzerman, Maarten J.; Wong, Peter K.; Higashi, Mitchell K.; Engbers, Jordan; Wiebe, Samuel (2016-02-01). "Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research". PharmacoEconomics. 34 (2): 115–126. doi:10.1007/s40273-015-0330-7. ISSN 1179-2027. PMID 26497003.

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