Bringing Data-Driven Decisions to the Healthcare Industry

Updated on January 16, 2021
Singh CJ headshot 2020 copy

Photo credit: Depositphotos

By CJ Singh

COVID-19 illuminated the harsh consequences of the healthcare industry’s slow embrace of technology to enable data-driven decisions. As a record number of people fell ill, the demand for personal protective equipment (PPE), ventilators and other supplies soared and healthcare professionals struggled to gain timely, accurate information about critical supplies, best practices for treatments and details regarding local surges. 

It’s an understatement to say the pandemic accentuated the importance of data’s role in healthcare. For more than a decade, our industry has been inching toward fully automating its business processes to improve efficiency and eliminate waste. The shift to a value-based care model has put even greater emphasis on data to help healthcare finally understand the true cost of delivering care, including the cost of supplies, anticipated patient outcomes and how to reduce unwarranted clinical variation. 

The financial impact of COVID-19 has been catastrophic for the healthcare industry. The American Hospital Association (AHA) expects hospital financial losses, which were already extreme due to the cancellation of elective procedures in the early stages of the pandemic, to deepen by an additional $120.5 billion from July 2020 through December 2020, bringing total losses to at least $323.1 billion for the year. As the industry charts a path toward financial recovery and future resiliency, accelerating the adoption and integration of more modern, data-driven technologies, as well as leveraging the power and flexibility of the cloud, is critical to solving the healthcare supply chain’s complex challenges. 

Clinically Integrated Supply Chains: The Path to Visibility and Transparency 

The push to improve efficiency, reduce costs and understand the true cost of care are the drivers of recent investments in electronic health records (EHR), enterprise resource planning (ERP) systems and digitizing supply chains. The next step in healthcare’s technological transformation is the integration ofthese IT systemsto support a clinically integrated supply chain. When organizations are able to feed accurate, evidence-based information into a hospital’s systems, providers gain greater insight and realize benefits, such as:

  • The creation of a single source of trusted information to better steward the use of drugs and devices in the delivery of care
  • The ability to align purchasing data and product utilization decisions with evidence
  • Greater visibility into the cost and clinical outcomes associated with medical products

The foundation of clinically integrated supply chains is the exchange of clean, accurate data that identifies factors affecting cost, quality and outcomes. Healthcare is unique given the number of stakeholders – providers, manufacturers, distributors, GPOs and payers, to name a few – that produce and work with data. Compounding the issue, healthcare data has typically resided in silos, in different formats with systems unable to communicate. In short, healthcare has a data problem: our industry has huge volumes of data but very little ability to draw the necessary insights to make informed decisions. 

The good news is this problem can be solved. The first step is for IT teams at provider organizations to establish a modern data strategy that integrates data and ensures it is clean, accurate and can flow easily across systems. A modern data strategy rests on four pillars: 

Data Platform: Investment in a data platform with real-time streaming that allows for seamless ingress and egress of data across systems is essential to ensuring the timelieness of the data that is feeding analytics and decision making.

Data Maturity: The siloed nature of healthcare data results in data that is fragmented, redundant and outdated. To answer foundational questions regarding overall business performance or patient outcomes, it’s critical to improve data integrity and data quality around core entities, including patients, products, providers, suppliers, procedures and facilities. Establishing ways to measure fill rate of key data attributes and match rate of duplicate entities are key parts of establishing data maturity and building out a strong master data management practice. 

Governance: Once data is clearly defined, organizations should establish a data governance program to ensure the ongoing quality and integrity of the data. In healthcare, a single entity, such as a procedure and its related procedure codes are relevant to multiple stakeholders. To avoid the pitfalls of too many cooks in the kitchen, establish a team dedicated to data governance that can determine how data assets can be used and accessed.

Security: Protecting data in healthcare is important because the data collected often includes personal, private patient information. With forethought, security can be built into a modern data architecture that allows an organization to balance data access to multiple stakeholders while keeping security and data rights top of mind. In addition to general concerns around network security and other aspects of a mature security program, there are several other critical factors to consider. For example, maintaining immutability of original data, establishing role-based access control to data as it runs through an organization’s data pipeline and documenting the changes taking place in the data as it’s transformed are all key concerns. Simply put, data is the new oil of the global economy. If we don’t invest in security from the beginning, we run the risk of causing damaging oil spills. 

Without a modern data strategy in place, organizations run the risk of analyzing siloed or incomplete data, jeopardizing supply chain initiatives and harming the partnership between supply chain and clinicians. With the help of clean and clinically-aligned data, supply chain teams can better understand product utilization and uncover opportunities for those products that will yield the best results for patient outcomes. 

Shifting to the Cloud: Key to Operational Efficiency  

Healthcare has been slow to embrace cloud technologies given the sensitive nature of the data that flows through its systems. However, there is growing acceptance among healthcare CIOs, particularly as the volume of data grows exponentially and their organizations struggle to keep pace with the digital age. 

For many providers with aging, sometimes even proprietary IT infrastructures, as well as limited IT budgets, the cloud provides better scalability and a lower cost of maintenance. This frees up IT resources for more advanced projects, including system integrations within the organization as well as with outside systems and sources of data. 

One of the more notable cloud transitions is the migration to cloud-based ERPs, which is well underway. In fact, Gartner forecasts the ERP market will reach $44 billion by 2022, much of that driven by the adoption of cloud-based systems. While adoption of new technologies has been slow in healthcare, adoption of cloud-based ERPs is accelerating as hospitals look to build more resilient supply chains. Moving to a cloud-based ERP system will enable real-time integration with other cloud-based systems, such as electronic health records and supply chain processes, eventually leading to the formation of clinically integrated supply chains. In addition, integration between multiple cloud-based systems is typically standards based, which reduces the cost and increases the speed of integration. In the quest to fortify supply chains, the winners will be determined by how fast they adopt cloud-based ERPs.

Simply investing in cloud-based ERPs will not facilitate data-driven decision making in healthcare, however. To ensure success, organizations must prepare by developing a solid data foundation and ensuring that its business processes are fully automated and interoperable so that the data feeding the ERP system will allow for more informed clinical and business decisions. 

One thing is certain as we evaluate the future of healthcare: data will be at its heart. As the industry turns to data to balance cost, quality, outcomes and finances, healthcare systems need to rethink their IT strategies. Whether it’s COVID-19, seasonal events like hurricanes or other unanticipated events, the need for data-driven decision making in healthcare is becoming more critical. To remain competitive and ensure business continuity, healthcare organizations must invest in the technological infrastructure to provide this analysis. Only with this in place can we move toward an era of using data to understand the best products, supplies and practices to achieve optimal patient outcomes and save our industry from further financial pain. 

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CJ Singh is Chief Technology Officer at healthcare supply chain leader, Global Healthcare Exchange (GHX).

The Editorial Team at Healthcare Business Today is made up of skilled healthcare writers and experts, led by our managing editor, Daniel Casciato, who has over 25 years of experience in healthcare writing. Since 1998, we have produced compelling and informative content for numerous publications, establishing ourselves as a trusted resource for health and wellness information. We offer readers access to fresh health, medicine, science, and technology developments and the latest in patient news, emphasizing how these developments affect our lives.