Demystifying Healthcare Data Governance: An Executive Report

Master patient identifiers and master provider identifiers are among the most important. In the absence of those two identifiers, almost all downstream analytic use cases will suffer or be impossible to achieve. These vocabulary data elements also represent a significant overlap of data elements among the primary source information systems in healthcare today. Therefore, resolving inconsistencies among these vocabularies will greatly facilitate the ability to integrate and analyze this data in the EDW. It is important to note that the organization does not necessarily have to impose vocabulary standards on the primary source systems, initially. That can occur more slowly and over a longer period of time because it involves significant changes to those primary source systems, most of which will be constrained by commercial vendors. The best alternative initially, is to utilize tools and methods within the EDW to map and enforce these vocabulary standards.

Healthcare Data Governance

Where to Start, Clinically?

Identifying where to start clinically is considerably more complicated than where to start from a vocabulary perspective. The seven criteria for choosing a clinical data opportunity are the following:

  1. Comprehensive agreement about the data definition of the disease state. An organization cannot improve what it cannot measure, and it cannot measure what it cannot agree upon measuring.
  2. Persistent agreement about the data definition of the disease state. It is not enough to agree on a comprehensive and widespread basis about the data definition of the disease state. The organization must also agree persistently. The agreement cannot be volatile.
  3. The availability of data in the organization’s data ecosystem to support the analysis of the disease state and the care processes around it. It makes no sense to target the improvement of a disease state or care process if the organization does not have the data to analyze the problem.
  4. Significantly meaningful patient volumes associated with the disease state. Unfortunately and realistically, an organization cannot afford to spend scarce resources on small volumes of patients unless those small volumes represent a significant overall cost to society, such as ALS.
  5. Significantly meaningful costs associated with treating the disease state. Fortunately, there is a general correlation between the severity of the disease or condition and the cost for treating it. Focusing data management and process improvement activities on high-cost diseases and conditions is common sense.
  6. Significant cultural interest in addressing improvements in either quality of care or cost of care, or both, for patients in the disease state. It makes no sense to undertake a complex data management activity and process improvement project if there is no cultural inspiration to do so.
  7. The ability to clinically intervene and show improvement for patients in that disease state. Once again, it makes no sense to undertake a complex project if there is little or no means technically, geographically, economically or otherwise to intervene for the purposes of improving the quality of care or cost of care associated with the disease or condition.

My empirical observations over the past 17 years indicate that the following areas of healthcare delivery seem to consistently meet the criteria listed above.

  • Pregnancy management, elective induction
  • Discharge medication adherence for MI/CHF
  • Prophylactic pre-surgical antibiotics
  • Materials management, supply chain
  • Glucose management in the ICU
  • Knee and hip replacement
  • Gastroenterology patient management
  • Spine surgery patient management
  • Heart failure and ischemic patient management

It is worth noting that the conditions and disease states listed above are heavily weighted towards inpatient, acute care processes. This is probably due to the relative difficulty in intervening in chronic condition diseases because so much of what affects those interventions lie outside the organization’s boundaries and scope of influence; those conditions are significantly affected by lifestyle and other socio-economic factors. Whereas, in an inpatient setting, the organization, in theory, has nearly complete control over the environment that surrounds the patient. Also, the data in a chronic-condition, outpatient-focused environment is very sparse in comparison to the data compiled during an inpatient encounter. Accurate and meaningful analytics depend on deep and voluminous data.

The diagram depicts this relationship between the degree of inpatient-centered care; and the availability of data and the opportunity for influence and intervention on a patient’s disease or condition.

Healthcare Data GovernanceData Governance: In Conclusion

Healthcare is on the brink of becoming a truly digital, knowledge delivery industry. The geography of care is shifting, making digital connections with patients more and more a requirement instead of an option. Five-star hotel-hospitals that encourage a long length of stay are, largely, a thing of the past. These hospitals are already seeing double-digit decreases in admission rates in those areas served by ACOs. The economics of healthcare has reached a tipping point. The industry can no longer operate with such high waste; various and numerous studies reveal waste levels of at least 30 percent to as high as 60 percent. The integration and analysis of data will play a critical role in helping healthcare organizations maintain a strong financial balance sheet while also improving the quality of care and health for their patients.

The Triple Aim of Data Governance is found in the constant and simultaneous management of data quality, data literacy, and data exploitation. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape.

Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance:

  1. Establishing the tone for becoming a data-driven organization
  2. Providing access to data
  3. Establishing data stewards
  4. Establishing a data quality program
  5. Exploiting data for the benefit of the organization
  6. The strategic acquisition of data to benefit the organization

As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.


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