A major concern for Healthcare CIOs for decades has been the development, procurement, and implementation of systems designed to capture data. As those systems have become more sophisticated, the controls around data capture have become more complex. There are module settings and required fields, and rules engines that allow you to configure those systems in a way that prevents omissions and errors. While IT has been involved in that configuration, and perhaps too involved at times, it has not always had accountability for “bad” data. However, now that Analytics platforms are in more widespread use, the quality of underlying data within an organization has risen in importance.
We have seen many examples of the quandary facing organizations from discrete data capture of lab values in your EHR to zip codes in patient registration. Looking at patient search as an example, a robust search will reduce costly duplicates which may consume hours of staff time per instance to resolve, and which may also open the organization up to HIPAA violations if incorrect information reaches the EHR and patient portal. However, a very robust search slows down patient access transactions which number in the hundreds or thousands per day, and which directly impact Patient Satisfaction scores, door-to-doc times, etc. How do you strike an appropriate balance?
For this and many other examples, a Data Governance structure is needed. Even the best Analytic tools will not solve underlying data issues by themselves. They will just provide elegant tools to highlight the issues. Like EHR implementations, a large portion of the value in Analytics platform implementations comes from the process re-engineering and data integrity work. The CIO and his/her team is probably in the best position across the enterprise to understand the implications and the flow of data from initial entry to reporting and analytics, and is therefore a critical member of such a structure. However, you can already see from the examples how important the Revenue cycle leadership, HIM leadership, Risk Management, Clinical leadership, Patient Experience, Decision Support, etc would be to resolution. You can also readily see how quality initiatives and payer contracts with downside (or upside) risk would require sound data to evaluate options and compliance.
HIMSS and AMIA among others have long championed this issue, and organizations have always acknowledged the issue in select areas. Data governance has never been outside of the scope of the CIO, but it is now more fully recognized as a strategic value-add, and an area where the CIO can and should be a proactive member of the C-Suite.