Healthcare data management: Critical challenges. Incredible opportunities.
The last two decades have been transformational. Information technology in general has brought about monumental progress in automating all aspects of daily life. It is estimated that by 2018, some 50,000GB of data will be added to our vast and growing IT world every second. This tremendous amount of data has been extremely beneficial specifically to the healthcare industry, both at an individual and macro level, in predicting trends in illnesses, research and cures. Yet, how best to manage this data?
New means and techniques are being devised to harness, examine, and utilize information locked in the staggering volumes of data being generated. Key to managing “Big Data” is to first analyze the healthcare business holistically in order to identify overlaps between processes supported by each player: providers, payers, pharmas, PBMs, patients and government bodies. This is a key initial step to making a substantive impact in improving patient care. It is vital that data associated with each of these processes be effectively managed to have any chance at ensuring quality and positively impacting the well-being of humans.
A sizeable portion of healthcare relies on benefits, claims and patient information necessary for every interaction or exchange in the healthcare continuum. In order for the healthcare industry to move forward and function as a seamless unit – providing accurate patient care, benefits, claims and patient information – the data management challenge must be solved through a free and accurate exchange of information. See Figure 1.
There are several players in this healthcare continuum, including pharmaceutical organizations, clinical trial organizations, therapeutic commercialization organizations, providers, payers, pharmacy benefit managers and government bodies such as the FDA and CMS. Due to the nature and volume of the information gathered by each of these key players, such as personal and confidential medication information, patient data, and treatment information, there is a high degree of overlap and redundancy. This can, and often does, lead to inaccurate data capture and duplicative information. As technology continues to move forward, and as future generations demand transparency and patient-centered care, the need for the accurate, timely capture and maintenance of data has never been greater for healthcare players. Enter: Master Data Management (MDM) – long considered by many a “silver bullet” for solving many of these problems.
Master Data Management, as the name implies, requires a “master” copy of data be maintained. In business terms, this puts the owner of the “master” copy in an advantageous position; however, this may or may not be in the interest of all of the players who participate in providing healthcare. A case for MDM solutions will always exist, but in the context of healthcare, free and accurate information exchange will always take precedence over data ownership by different participating organizations. To enable data exchanges between healthcare entities, standards are being supported by various organizations such as HITECH, NCPDP, NCHS, NIST, and many more. The requirement to adhere to industry standards is a great step forward; however, the implementation of these standards throughout various integrations will require a comprehensive strategy that provides a uniform, consistent and efficient approach.
Implementation has to take into account the key realities of the market: cost effectiveness, speed to market and addressing quality concerns that make implementation solution(s) commercially viable. Benefits are a core system of existing healthcare programs in the United States. Every patient avails services from providers such as prescribers, pharmacies, hospitals, nursing homes, etc., using benefits covered by policies underwritten by payers, or through government programs such as Medicare and Medicaid. As such, the challenge of managing of benefits, when overcome, allows organizations to leverage this information to integrate participating entities.
A close second to benefit management is the challenge of determining appropriate data conditions, such as claims, to establish the accuracy of benefits. Most organizations rely on covering obvious risks, such as patient dissatisfaction, guarantee payouts, and regulatory non-compliance through limited validation of benefits. Approaches such as benefits scenario generation, claims generation and prioritization of scenarios are getting more attention and traction in the industry given that auditable coverage of exposure is achievable in short order.
Mergers and acquisitions are also very real part of the healthcare industry today. Huge initial expenses are often necessary to meet “Business Case” requirements. Quite often, budget overruns occur when systems and data migration is done manually and detailed planning an afterthought to the merger. Instead, a playbook for migrating such systems and data should be tailored prior to the merger as a part of the business case. Management of data from and to the systems being merged is one of the biggest challenges, with roots lying in manual or incomplete analysis of the data and the relationships between data entities. A specific pre-plan ensures the migration plan and required automation are well established and realistically achievable, to satisfy the business case.
As solutions for providing quality and cost management continue to evolve, so does our understanding of the problems intrinsic to the healthcare continuum. This understanding drives innovations that enable patient interactions to be viewed as one continuous transaction. Continuous Integration (CI) is a well-known term in the IT industry. Moving forward, Continuous Integration must be expanded to focus on the business transactions versus leveraging CI to ensure systems accuracy alone. Transactions that span just one system or database work perfectly; however, when a transaction spans across a wide spectrum of a particular entity’s systems, departments and organization leave a trail of duplicative, incomplete and incorrect data in their wake. This seriously undermines and compromising data integrity across the enterprise. Close analyses reveals that management of data related to medications, medical devices, patients, benefits and claims has to be viewed and dealt with holistically through automation, plus, systems processing the information must be integrated in ways where outcomes are modeled and certified before the patient is put at any risk. To be sure, the imperative for today’s healthcare world is clear: Properly utilizing IT to affect the timely, efficient and accurate sharing of data and information across a wide spectrum; in turn enabling positive patient – and business – outcomes.
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