By: Kathryn Puczkowskyj
Imagine a day when all of your health information is fully integrated. Your history – each doctor’s visit, test, lab, or physical complaint – is stored in one location where any healthcare provider you see has access to it. You are saving money on testing, and your doctor is not limited to relying on your memory of prior diagnoses because she can look them up. There are such rapid advancements in healthcare technology today, one would think that this scenario isn’t too far in the future. However, a world of fully-integrated patient data has some wrinkles that need ironing out before it becomes reality. This writing seeks to explore the current technologies and databases that would make this scenario possible, and how they are being used in Montana.
Saving Money by Digitizing Patient Records
The electronic health record (EHR) is the beginning of the digital story of any patient’s health history. This digital file contains a longitudinal record of health information generated by patient encounters with healthcare professionals in any setting (Menachemi, Nir, & Collum, 2011). EHRs allow providers to modernize, digitize, and become more efficient in their administrative processes by moving away from paper records. Not only does digitization save office space by not having to store paper files, but it also puts historic records literally at their fingertips and eliminates the plague of poor provider penmanship.
In addition to these benefits, EHRs have three particular functionalities that “hold great promise in improving the quality of care and reducing costs at the health care system level:” computerized physician order entry (CPOE), clinical decision support tools (CDS), and health information exchange (HIE) (Menachemi, 2011). CPOE tools allow providers to digitize orders for services like tests, labs, and imaging. These requests go straight from the provider’s device to the facility where the orders are carried out – typically a lab or imaging facility. CDS tools use machine learning to assist providers in making treatment and diagnosis decisions, lessening burnout, and helping avoid errors or adverse events (Bresnick, 2017). HIEs are centralized databases in which participating providers share EHR data in order to facilitate patient care, cut costs, and eliminate waste (Jason, 2019). Sharing patient data across a state, for example, could prevent a patient from getting duplicate lab and imaging services. In 2010, one study found that as much as 20 percent of cases in a sample of EHRs had “at least one duplicate test not clinically indicated” (Stewart, 2010). If a doctor does a screening at an annual exam, there may be no need for another provider to order the same screening when the patient is seen later that year. By linking the patient’s EHR, the second provider is able to see the test results and make decisions based on those test results. Patients not only receive cheaper care, but they also are saved a lot of time, effort, and discomfort.
The use of CPOE, CDS, and HIE are all included in the improvements proposed by the Affordable Care Act, as well as in the incentive programs administered by the Centers for Medicare & Medicaid Services (Hinrichs & Zarcone, 2013). The first two tools are accessible to providers via hospital systems or they can be acquired through the purchase of customizable products. HIEs, however, are typically state-based projects that are start as a collaboration between healthcare organizations and grow into a connected network of patient and provider data.
Montana Big Sky Care Connect
There’s no doubt that Montana is a rural state – it is the fourth largest state in area with sparse population (“PNR Region”). However, the Big Sky state’s patients can benefit just as much as patients from urban states from greater interconnectivity in medical care. At least one study has demonstrated the feasibility of state-based health information databases “to develop comparable healthcare performance measures that inform state, regional, and organizational healthcare policy” (Diaz-Perez et al, 2019).
In 2019, Montana received a $19 million grant from CMS as part of a 90/10 federal match with Big Sky Care Connect (BSCC), a non-profit organization piloting an HIE program in Montana (Jason, 2019). Among other benefits, BSCC says that they will:
- improve quality of healthcare in Montana by aiding in coordination of care, providing quality reporting and tracking, enabling new value-based payment models, and providing convenient connection to drug registries
- provide better care by enhancing emergency response, reducing adverse drug events, and supporting integration of behavioral health
- reduce costs by avoiding redundant tests and procedures, streamlining administrative tasks, and reducing hospital readmissions (“Big Sky Care Connect”).
Its website tells patients that their information will automatically be added to the HIE if their provider is participating. BSCC follows an opt-out policy, and the provider is responsible for making sure the patient is informed of where their data is being sent.
The pilot program in Montana has some buy-in from providers and they have one large, commercial payer participating. However, they have yet to receive commitment from other large payers in the state. In order for a HIE to give a complete picture of a patient’s health history, all of the patient’s health history must be included. Insurer claim data can provide some additional pieces of information that provider records do not. To understand why payers are more hesitant to share claims data with HIEs, the next section will discuss a similar network to the HIE that has not yet been established in Montana, the All-Payer Claims Database (APCD).
Insights from Patient Claims Data
APCDs are large state databases that include medical, pharmacy, and dental claims combined with eligibility and provider file data (AHRQ). The difference between an APCD and HIE is that HIEs are based on complete provider medical records, while APCDs are based on payer claim data and potentially provider records that are sent in to support specific claims. These databases can “provide invaluable information about the drivers of cost, the value of health care interventions, and the efficacy of policy initiatives,” while also forming the “foundation for the development of price and quality transparency tools” (Kelly & Andrew, 2017). The data in APCDs are reported to the state by insurers, usually as part of a mandate by the state. The data requirements to research downstream effects of insurance switching and provider utilization made this type of research previously infeasible, but APCDs offer an answer (Graefe et al, 2017).
Despite their benefits, APCDs (and HIEs) may stumble on some key requirements. First, there is a vital need for privacy and security. Patient data found on claims and in provider files are protected under federal laws like HIPAA (the Health Insurance Portability and Accountability Act), and must remain confidential (Kelly & Andrew, 2017). Second, there may be a need for confidentiality in contract rate reimbursement negotiated between the providers and the payers due to existing contract language. Thus, an APCD would need to figure out how to limit access to payment information normally tied to claims data. Third, there is a need for a unique identifier for each patient in the database. In order for providers and payers to link data to a patient, they will need to be able to identify the patient. However, payers have myriad ways to come up with member IDs, and hospitals have their own account numbers. APCDs (and HIEs) would need to come up with a unique identifier that did not include any personal identifiers, and that remained consistent over time.
In Montana, none of the databases proposed to date have found ways to adequately address these requirements to meet the needs of all key stakeholders. In addition, although a 2011 Montana HB0573 authorized a feasibility study into the creation of a statewide APCD, the 2016 HB620 to create an APCD died in committee.
The Prospect for Montanans
Remember our dream of a day when healthcare is fully integrated? Montanans have not yet realized that dream, but we are making progress. Some hospitals like Billings Clinic offer bill estimate tools on their websites, and many payers offer price transparency tools based on claims data for members to “shop” services at different facilities. These options offer a limited “guesstimate” of what patients will be asked to pay for care, but not all of the pertinent data is being used to calculate a fully informed, precise quote. Additionally, only providers integrated into a large hospital system like Providence are able to share patient history. Thus, patients seeking care from those systems may have nearly seamless care, but any providers outside of that system will be missing valuable medical history.
Comprehensive databases to house patient health history and claims data would greatly improve the transparency of today’s healthcare landscape. The potential to cut wasted time, effort, and ultimately costs for providers, patients, and payers alike is significant. Streamlining the healthcare journey by sharing data across organizations could lessen administrative burden and minimize physical and financial stress for patients. Montana is working toward finding this “sweet spot” of data sharing, but there are still some obstacles that will take some trust and compromise between all sides in order to overcome. Before this dream can become reality, this state will need to address myriad privacy and access issues for all stakeholders.
- “All-Payer Claims Databases.” AHRQ, www.ahrq.gov/data/apcd/index.html.
- “Big Sky Care Connect.” Big Sky Care Connect, http://www.mtbscc.org/.
- Bresnick, Jennifer. “Understanding the Basics of Clinical Decision Support Systems.” HealthITAnalytics, HealthITAnalytics, 12 Dec. 2017, healthitanalytics.com/features/understanding-the-basics-of-clinical-decision-support-systems.
- Diaz-Perez, Maria de Jesus, et al. “Producing Comparable Cost and Quality Results from All-Payer Claims Databases.” The American Journal of Managed Care, U.S. National Library of Medicine, 1 May 2019, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613782/.
- Graefe, T., et al. “Using All-Payer Claims Databases to Study Insurance and Health Care Utilization Dynamics.” Journal of General Internal Medicine, Springer US, 14 July 2017, link.springer.com/article/10.1007/s11606-017-4128-5.
- Green, Linda, et al. Realizing the Potential of All-Payer Claims Databases. Freedman HealthCare, LLC, Jan. 2014, shvs.org/wp-content/uploads/2014/11/RWJF_SHVS_RealizingPotentialAllPayerClaimsDatabases1.pdf.
- Hinrichs, Steven H, and Patina Zarcone. “The Affordable Care Act, Meaningful Use, and Their Impact on Public Health Laboratories.” Public Health Reports (Washington, D.C. : 1974), Association of Schools of Public Health, 2013, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3729999/.
- “Interactive State Report Map.” APCD Council, 17 June 2015, http://www.apcdcouncil.org/state/map.
- Jason, Christopher. “Montana Receives $19M Grant to Fund Health Information Exchange.” EHRIntelligence, 6 Sept. 2019, ehrintelligence.com/news/montana-receives-19m-grant-to-fund-health-information-exchange.
- Kelly, and Andrew. “All-Payer Claims Databases: The Balance between Big Healthcare Data Utility and Individual Health Privacy.” SSRN, 20 Oct. 2017, papers.ssrn.com/sol3/papers.cfm?abstract_id=3054240.
- Menachemi, Nir, and Taleah H Collum. “Benefits and Drawbacks of Electronic Health Record Systems.” Risk Management and Healthcare Policy, Dove Medical Press, 2011, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3270933/#b7-rmhp-4-047.
- “PNR Region.” NNLM, nnlm.gov/pnr/about/montana.
- Stewart, Bridget A, et al. “A Preliminary Look at Duplicate Testing Associated with Lack of Electronic Health Record Interoperability for Transferred Patients.” Journal of the American Medical Informatics Association : JAMIA, BMJ Group, 2010, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995707/.