2. Interoperability and Cost Barriers to Digital Health
When you move between healthcare spaces, your health records should move with you. But, if you do not take the necessary steps, for example signing Release of Information documents, there can be a lapse in time before new providers have access to your medical history. This can create new sets of problems on top of the existing medical issues; medication management suffers when medications are missed, diagnostic tests are repeated unnecessarily, or follow up care is halted until your new team of providers agrees to follow an existing plan or comes up with a new one.
But even if you don’t switch providers, there are also many instances where data sharing between agencies is necessary, specifically for care coordination. With a shift from volume to value, data sharing is imperative to reaching measure compliance goals and successful outcomes.
There is no argument that safeguarding our medical and personal data is a vital action; however, certain aspects of data guardianship can become a barrier when information needed to coordinate chronic or emergency care with agencies that are not the authors of the data. So, how can digital health be useful if the data that is collected cannot be appropriately shared with entities that need it?
Interoperability could be the solution, but there are barriers such as identifying patients across the continuum of healthcare spaces, information blocking, and standard language for data exchange.
a) Unique Patient Identifier (UPI): “We have to make sure you are who all your records say you are…”
Imagine having the task of completing a puzzle titled “Your Health” without having all the pieces. However, when collecting the rest of the pieces, there is a challenge of verifying that they actually are your health data. The issue of individuals having data points in different places creates critical safety conditions because in order to receive safe and proper care, all of the data has to belong to you unequivocally.
While having an UPI has worked for data collection and interoperability in some northern European countries, in the U.S a ban disallowed “planning, testing, or developing…” any type of solution that would lead to the creation of a national patient ID.
However, advocacy by groups such as AHIMA, CHIME, Intermountain Healthcare, and Blue Cross has seen a reversal in the federal government’s reluctance to explore this solution.
b) Information Blocking: Ethical issues in data sharing
Care Coordination and Population Health Management are becoming critical tools in the transition to Value Based Pay. However, it is still common for organizations to utilize antique methods of collecting up to date patient data to track and act on patient needs. Also, even if staff have access to modern systems, barriers placed by some data sources result in incomplete records, poor outcomes, and a decrease in revenue. One of these challenges is Information Blocking.
Information Blocking refers to the collective actions taken by organizations or individuals that result in interference of health data between disparate parties. The Office of the National Coordinator for Health Information Technology (ONC) provides these examples:
- Fees that make data exchange cost prohibitive.
- Organizational policies or contract terms that prevent sharing information with patients or health care providers.
- Technology is designed or implemented in non-standard ways that inhibit the exchange of information.
- Patients or health care providers become “locked in” to a specific technology or health care network because data is not portable.
Recently, the CMS created steps to reduce information blocking within certain quality payments initiatives such as MIPS. Three statements of attestation addressing information blocking are now required for certification.
c) Standardization: There’s an API for that
True interoperability depends on the correct exchange of health data. This exchange must occur seamlessly and accurately, and it does so by following particular standards for all clinical messaging. These standards facilitate the encoding of health information using common, previously agreed upon language that can be read by different systems.
Standards are comprised of two main concepts in IT language: syntax and semantics. Syntax defines the grammar rules for the language used so clinical messages can be read properly by the receiving system. Some examples of syntaxes are Health Level 7 (HL7), Digital Images and Communications in Medicine (DICOM), and National Council for Prescription Drug Programs (NCPDP). Semantics refers to the coding of the message so the content makes sense when the message is deconstructed. Examples of these include Current Procedural Terminology (CPT), International Classification of Diseases (ICD), Logical Observation Identifiers, Names, and Codes (LOINC), and Systematized Nomenclature of Medicine (SNOMED-CT).
When a health organization is selecting tools to collect, keep, and distribute medical information it must ensure that those systems follow proper standards and encourage their use when interfacing with other systems. During information exchanges with disparate systems, information can still be exchanged via an Application Programming Interface (API). API’s allow two different software systems to communicate in an automated and configurable way.
d) Cost: How much?
According to the National Health Expenditure Accounts (NHEA) health care spending in the U.S. grew 4.3 percent in 2016. This equate to $3.3 trillion or $10,348 per person.
Compared to other industrialized countries, we spend more while failing to improve health outcomes. With nearly ninety percent of the population having healthcare coverage in 2016, we spent almost eighteen percent of the GDP on health care. When measured against the nine and twelve percent spending of Australia and Switzerland respectively while covering ninety nine percent of its population, it is obvious that they not only are covering more individuals, but are spending less in doing so.
Some of the reasons we spend so much on health care are our insatiable appetite for expensive, repetitive exams and procedures, prescription drugs, and in-patient hospital care. So when it comes to adding the costs of implementing new technologies in healthcare, organizations must ask themselves if they have a clear definition of what their ROI will be from this new tool.
Purchasing a new tool requires costs to be spread out over time and categories. Besides the price of the hardware/software, money has to be allocated for training, maintenance, and upgrades.
Whether we come up with a UPI, resolve data sharing dilemmas, or adopt exactly the same standards, organization will still be faced with the challenge of addressing internal workflow processes that relate to innovation. Transition to value based pay methods require deep and honest assessments of how organizations plan their future in delivering care.
Centers for Medicare Services. (2017, October). The Merit-based Incentive Payment System (MIPS) Advancing Care Information Prevention of Information Blocking Attestation: Making Sure EHR Information is Shared. Retrieved July 15, 2018, from ww.cms.gov: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/ACI-Information-Blocking-fact-sheet.pdf
Heflin, E. H. (2018). A Framework for Cross-Organizational Patient Identity Management. Retrieved July 10, 2018, from www.sequoiaproject.org: https://sequoiaproject.org/wp-content/uploads/2018/06/The-Sequoia-Project-Framework-for-Patient-Identity-Management-v31.pdf
Sood, H. B. (2018, February 21). Has the Time Come for a Unique Patient Identifier for the U.S.? New England Journal of Medicine – Catalyst, p. 3.
The Office of the National Coordinator for Health Information Technology (ONC). (2017, September 14). Information Blocking. Retrieved July 5, 2018, from www.healthit.gov: https://www.healthit.gov/topic/information-blocking
U.S. Centers for Medicare & Medicaid Services . (2018, January 1). National Health Expenditure Data. Retrieved July 29, 2018, from www.cms.gov: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.html
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