Murali is the Co-Founder/CEO of Entrigna, inc., a real-time decisions software specializing in real-time IoT and Big Data custom solutions

Healthcare is an information-intensive sector, meaning data plays a crucial role in organizational and operational decisions. The sector’s information management has been traditionally centered around electronic health records (EHR) systems, and providers are still figuring out what the underlying EHR systems offer with clinical workflows, processes and capabilities.

Although healthcare is a complex system with petabytes of data potentially generated weekly to monthly, as the co-founder and CEO of a data solutions company, I’ve observed that most healthcare organizations are not mature enough to take advantage of the data right away. There is still a massive deficit in vision, strategy and planning for mature ways of data acquisition, data sources integration and data storage. All organizational stakeholders, including operational and technology stakeholders, need to align their mission and goals to promote joint data-driven initiatives.

The Need

Several healthcare organizations have substantial administrative costs, including out-of-pocket expenses because of the management of poor patient admissions, length of hospital stay, sub-optimal infection control and lack of planning for any outbreak. The grave and unfortunate situation presented by the Covid-19 pandemic was a rude awakening even for well-established healthcare organizations in their preparedness to react to unprecedented demand.

Government mandates to implement Medicare, Medicaid and other health plans can also add to healthcare costs. Organizations can shift their focus toward preventive and personalized care, predictive care-based population health initiatives, data intelligence-based internal operational decision support and intelligent remote monitoring of patients leveraging the internet of medical things to control and optimize costs. However, all such initiatives need fast and effective decision-making to realize the actual value of the data.

Furthermore, most clinical systems are transactional and configured for real-time access and not necessarily for bulk data retrieval needed for clinical/operational business intelligence. Even though the need for data analytics gets realized, the lack of readiness and availability of integrated data with a holistic, integrated set of analytic capabilities can severely hinder healthcare organizations from taking proactive actions.

The Challenge

Medical records typically contain free-form text such as unstructured patient notes, information about medications, medical orders and discharge summaries, to name a few. Besides, massive amounts of medical imaging data such as radiology, cardiology, oncology and pathology images coexist alongside healthcare operations data. However, traditional healthcare information systems are mostly packaged systems procured from different vendors to serve specific operational functions and get deployed in a siloed manner without any integration and interoperability, resulting in siloed data generation and storage. There have been attempts to mine data using methods such as traditional data warehouses; however, such practices don’t suit and scale to the needs of varying formats of structured, semi-structured and unstructured data.

Several other factors contribute to a lack of data analytics and business intelligence maturity. Research evidence shows that most healthcare organizations lack skilled analytic resources, technical integration across multiple platforms, systems of truth for critical data and information entities, and data governance, which are vital prerequisites for implementing data platforms. There are also organizational cultural aspects such as end-user adoption, strongly perceived usefulness of data, required transparency in reporting metrics and general acceptance of being measured using data. Finally, there is a great need for finding a synergy linking business outcomes with technical implementations.

The Recommendation

Firstly, healthcare organizations must realize that the future will be heavily data-driven. There are emerging fields of genomics, radiomics and precision medicine that deal with the analysis and processing of massive amounts of data. Very soon, I believe all healthcare organizations will have to embrace such emerging practices for more significant benefits to advance patient care.

Secondly, healthcare organizations must come out of the clutches of healthcare software vendors that monopolize and hoard critical data for their own self-serving goals. Software vendors’ usual rhetoric is about patient data privacy and the risk of sharing such data. In my opinion, it is essential that such data is made interoperable and shared across healthcare’s internal systems and shared with external trusted partners, albeit de-risked and de-identified. History has proved more than once that sharing data leads to discovering new knowledge and thereby enabling innovation.

Thirdly, healthcare organizations must be more entrepreneurial in investing in and sponsoring innovative data-driven initiatives. They must shun the typical laid-back mentality that they can wait until some capability becomes a packaged commodity. Instead, they can drive innovation from within and incubate new capabilities before such capabilities are ready for general consumption.

Fourthly, healthcare organizations must look inside for domain knowledge. Even though there are key subject matter experts with considerable data domain knowledge, such as clinical informatics professionals and medical data analysts, their primary focus has been on supporting patient care operations and clinical workflow management. Such clinical domain experts can transform healthcare data collection processes to prevent data silos, enable complex analytical models for deriving insights and then ensure consistent, timely and effective communication of resulting information.

Finally, all the key decision-makers of the healthcare organization must align to a joint mission, vision, goals and objectives before undertaking any such data-enabling initiatives. Such alignment entails great collaboration, concerted efforts and consensus at the enterprise level to define commonly-accorded business, technology and implementation strategies. Strategy implementation will not happen overnight but is typically a multi-year initiative backed by a shared vision and a commonly agreed on intentional action plan. Therefore, it is imperative for all critical decision-makers across the enterprise to see one common horizon in such a multi-year perspective.


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