Critical Healthcare M&A Strategies: A Data-driven Approach (Executive Report)

Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:

  • Establish the strategic objectives of the M&A with the leadership team.
  • Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
  • Identify the near-term data strategic priorities, stakeholders, and tools.
  • Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.

When Healthcare Data Analysts Fulfill the Data Detective Role (White Paper)

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

  1. They are inquisitive and relentless with their questions.
  2. They let the data inform.
  3. They drive to the heart of what matters.

Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.

The Changing Role of Healthcare Data Analysts—How Our Most Successful Clients Are Embracing Healthcare Transformation (Executive Report)

The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.

Demystifying Healthcare Data Governance (Executive Report)

Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy. The Triple Aim of Data Governance is: 1) Ensuring data quality; 2) Building data literacy; and 3) Maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.

Accountable Care Organizations Drive Demand for Data Warehouse (Executive Report)

Analytics packages offered by their EHR vendor and their existing business intelligence/analytics tools are not up to the task of supporting the transformation currently underway. Adaptive data warehouses and the analytical tools now available provide crucial, actionable intelligence that health system clinicians can use to identify opportunities to improve clinical effectiveness, cost effectiveness and safety.

7 Essential Practices for Data Governance in Healthcare (Executive Report)

Data is now one of the most valuable assets in any organization, especially healthcare as we transition into a more analytically driven industry. If we accept the assertion that healthcare is a knowledge delivery industry—that is, the application of specialized skills and knowledge, along with specialized tools—it is our obligation to exploit the data assets in our environment to augment and optimize that knowledge and those skills.