Healthcare is one of the last industries where you hear the term MDM (Master Data Management) mentioned. Most IT industry analysts, software firms and consulting organizations are geared towards your typical company that sells products to people or businesses.
MDM examples are always getting a master list of products or cleansing your way to a consistent list of customers, which is not exactly the mindset of healthcare organizations. But lack of MDM is precisely what is adding untold costs on healthcare organizations (and ultimately on all of us) and inhibiting these organizations from improving the quality of health care services at an affordable cost.
Let’s divide the healthcare industry (simplistically) into insurers and providers (we will position pharmaceuticals, biotechs and medical device companies as life sciences). Many of the large insurers have invested in data warehousing and data integration, but smaller insurers, i.e. regionally based HMOs (healthcare maintenance organizations) and healthcare providers, such as hospitals and physician groups, have fledgling efforts or have been bogged down in many of the issues below.
Healthcare organizations have significant data consistency issues regarding the following data subjects:
- Patients
- Physicians
- Procedures
- Diagnosis codes
- Service Rates
- Pay for Performance (P4P) measures
Each insurer and each healthcare provider tracks this data differently. The problems are magnified because healthcare is regulated on a state by state basis along with federal and industry regulations. Throw in privacy and security concerns to exacerbate what healthcare groups need to deal with.
Most healthcare organizations, even large ones, are an affiliation of generally small physician groups. These groups may be your local doctor’s office, i.e. primary care physicians (PCP), specialists or emergency room (ER) providers. Often these groups do not have a lot of IT resources at their disposal.
Data flow is often flat file transfers between insurers and healthcare provider organizations, as well as from the individual physician groups and the larger provider organization. These flat files are generally not standardized and change each year when contracts are renegotiated between insurers and providers. This is an industry where you typically are not in control of your source data. It is thrown at you and you have to deal with it.
The need for an MDM is significant at healthcare organizations. The benefits from MDM are
- Data consistency
- Productivity
- Enabling more cost effect patient care
It is remarkable when one looks at the amount of resources devoted to manually dealing with inconsistent master data throughout health care. People in this industry do an amazing job of dealing with it, but it is often a time-consuming manual effort with much reconciliation. Having an MDM program would improve overall productivity and enable organizations to process and react more quickly to patients, insurers, employers and physicians.
A hidden jewel of an MDM effort though is enabling health care organizations to provide more proactive care. I have seen healthcare providers develop data solutions oriented to specific populations of patients who have diseases, chronic conditions or are at specific risks. These solutions may be for diabetes or asthma, for instance.
By bringing in historical clinical or demographic data related to patients tied together through consistent master data and taking advantage of predictive analysis, health care providers can proactively help their patients rather than waiting for the next episode when the patient’s health has worsened. Many insurers are linking Pay for Performance (P4P) programs with these kinds of efforts because a healthier patient is a great goal by itself but better health also means lower health care costs.
The MDM silver bullet product has not been invented for healthcare industry but these organizations should not despair. There are concrete steps these organizations need to take.
- First, healthcare organizations need to examine where they are spending their resources on handling inconsistent master data and focus their efforts on those areas.
- Second, the efforts need to be in collaboration with business operations, physicians, insurers and IT, and need to involve defining master data and performance metrics.
- Finally, such organizations need to leverage their data warehousing and data integration efforts.
