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I’ve been fortunate to work in the field of Information Technology for over 25 years now, serving within virtually every discipline of IT for some of the world’s largest companies and across many different industries. When I came into the healthcare industry back in 2012, I was shocked by the IT landscape and wondered at how it could be so far behind other industries from a technology perspective. None of the industries I’ve served in the past faces the challenges that Healthcare faces today. The industry has largely gone surprisingly unregulated, and there are suddenly huge technological demands upon it that cannot now be quickly or easily met. The experience a patient looks for when in need of healthcare is a personal one, between themselves and a qualified caregiver whom they trust and respect. Delivering that experience has not, until relatively recently, required much technology.
Within the past decade, the Center for Medicare and Medicaid Services (CMS), alongside the Office of the National Coordinator for Health IT (ONC), powerful arms of the federal government, have made a concerted effort to introduce regulation and impose significantly more control within the healthcare industry. At first, this came in the form of the “Meaningful Use” (MU) program. MU was a program which in the earliest years of its existence aimed only at forcing hospitals and practices across the US to purchase governmentally ‘certified’ systems that would allow for the collection and reporting of patient data. Sizable financial incentives were paid to practices that could produce a report from one of these expensive “Electronic Health Record” (EHR) systems that had been ‘certified’ by CMS/ONC, even if they weren’t using the system and the report was empty. In the following phase, the MU bar was raised such that the financial incentives would be available only to practices who were able to report actual data on at least a defined percentage of their patient population, so practices and providers of care of all kinds were compelled to find ways to collect and enter this data into the E.H.R. system, and then supply that data to the government by a given due date. As time went on, the percentages of the patient population’s data required to be reported in order to get the incentives were increased, and eventually if your practice did not participate and deliver the data, significant penalties were imposed. These came in the form of reductions in reimbursement from Medicare or other government based payers for those patients covered by those programs. Medicare would withhold a percentage of the fee the doctor had historically been reimbursed for providing service to a patient if that doctor or practice did not report the data and participate in the MU program. The percentage grew larger each year. Thus the program moved from compelling change to coercion.
"The experience a patient looks for when in need of healthcare is a personal one, between themselves and a qualified caregiver whom they trust and respect"
While the US government was defining what would be included and tested in order for an Electronic Health Record system to become ‘certified’, and thus to be eligible for these monetary incentives, they did not include a forward-thinking data strategy and thus lost a one-time opportunity to support significant reduction of waste in the industry, and to later allow interoperability of health data. No single identifier, for example, was selected to be a unique patient identifier. Today the argument continues surrounding use of a person’s social security number, which is widely opposed, so the industry tries to match up records from various medical practices based on several data elements for a given patient. The process is severely flawed, the data fluid and subject to human error, thus a large percentage of the health data records lack integrity. Lack of a consistent unique patient identifier means no ability exists to create a single personal health record that could follow a patient throughout their lifetime and aggregate all of the illnesses and /or treatments that person may have. Because of lack of this visibility and a single record, tests are repeated and waste is rampant. Such a record would drive waste out and improve care and health immensely, so this was a significant oversight. Further, data definitions specifying length and type of each data element were not defined and required as a part of ‘certification’, which would have later made the systems readily able to exchange data. Today, one certified E.H.R. system may have a patient identifier field that comprises 10 alpha-numeric characters, whereas the next certified E.H.R. system might have an 8 character numeric only patient identifier, exacerbating an already complex and flawed data landscape within health data.
As an illustration of this, I look back to my years working with food products being warehoused and transported in over the road trucks. The GS1 created a set of very specifically defined, globally recognized data record elements that is used across all food products. Each data elements’ length and data type is defined and adhered to across the industry. This definition serves as the means by which businesses clearly electronically communicate all data. Every producer of any food product is required to register their product and is issued a specific unique identifier which they must use to barcode label their product following these strict guidelines and data definitions. This solid foundation allows for recall and tracing of food products, efficient receiving and shipping operations, automation of inventory and consumption data, and a myriad of other technology deliverables. Within a barcode printed on a case label of food products, elements to indicate what the product is, its age, its weight, its place of origin, and really everything one could want to know are incorporated. This is not only applicable to the case of food products in a warehouse or on in a truck, but also at the retail UPC code scanner in the grocery store. Very hefty financial penalties are imposed if a bar-coded label misidentifies any product at the grocery store checkout scanner. We can and have accomplished this data standardization for our food supply chain, but unfathomably, somehow we have not been able to do so for our healthcare data.