Improving Health Data Management—10 Proven Best Practices

Introduction

Invariably, the future of the healthcare industry hinges on our abilities to put to the best possible use the gargantuan amounts of data we have now become so adept at gathering and filing away.  Data storage, though, was never the endgame but, rather, the means by which to promote higher quality for a lower cost. Experts tell us, furthermore, that without the use of analytics we can’t possibly put to optimum use all that data, thereby ameliorating quality and creating a more efficient, accountable and cost-effective system.

As things stands right now, we’re literally burdened with terabytes and petabytes of data disseminated across a wide, disparate array of systems and facilities, even as the data continues to accumulate at a wild, seemingly out-of-control pace.

As the health industry scrambles to become move value-oriented, efficient health data management is becoming exceedingly important.  To that end, here are 10 ways that health data management systems and initiatives can be improved.

.10 Improving Health Data Management Best Practices

1. Establish and regularly update clearly-stated data management criteria regarding original intake, prioritization, triage and assignment of work for all documents/reports created by the organization.

2. Establish a “central business intelligence” consulting group on data governance & organizational-analytics.

This group can provide specialized services to each department; their findings/recommendations can help identify risks and inefficiencies.  Such guidance can assist in making organizational changes.

3.  Invest in the creation (or, possibly, sharing) of your own late-binding enterprise data warehouse (EDW).

This can be a critically important step in the designing of a stalwart analytic infrastructure.  In fact, an EDW can serve as a central, safe repository for data optimized/organized for analysis, measurement, and reporting.

4. Design EHRs systems custom-made for the special needs/requirements of specific  populations.

In view of the fact that electronic health records don’t seem to lend themselves well to one-size-fits-all paradigms, it’s best if EHRs are designed to adapt or conform on-demand, as the need arises or population needs/requirements change.

5. Participate in the trend to put surveys to more comprehensive/integrative uses.

Although health-information-gathering surveys have been around (e.g., National Health Interview Survey or NHIS) for eons, it is becoming clear that these mostly underutilized tools can be integrated more comprehensively than they are at present.  Questions of reliability have hampered enhanced uses but, used properly, surveys can provide information that is either impossible or difficult to gather using other methods.

6. Work toward standardizing direct data collection.

There is no question that one of the deficiencies of our present health data gathering system is the fact that direct data collection programs and systems are not adequately standardized.  Although concerns for data breaches, privacy and security have hampered progress, new technologies are proving these concerns to be unnecessary/counter-productive.

7. Use analytics to improve health data management.

How can analytics improve health data management?

Since the impetus right now is to lower costs, the improvement of supply chain management in healthcare facilities in order to increase revenue streams is one potent example.  Analytics makes is possible, for example, to keep tabs on materials/supplies in large, busy hospitals, thus reducing opportunities for theft, mismanagement and waste.

8. Use streaming analytics software.

Streamlining analytics software prompts providers to analyze large amounts of data as it becomes available.  It can help, among other things, to detect medical patterns potentially indicative of strokes, heart attacks, and other complications.

9. Employ “logic models” to strengthen the use of data in decision-making.

Too often collected health data sits somewhere unused or misused.  With the use of logic models, however, pathways (e.g., charts, Infographics, flow charts, etc.) can visibly depict how interventions and specific activities can streamline the use health data in decision making.

10. Make a better effort at engaging data users & producers.

One of the main deficiencies in the health data collection/management paradigm is the fact that there isn’t enough and adequate interaction between the data producers (IT personnel) and data users (professionals who gather and use data for decision-making).  When these people collaborate, however, everyone is more intimately aware of available data sources, collection processes/methods, and the need for quality across the board.

Conclusion

Clearly, the main reasons for improving health data management are the gradual but noticeable improvement of healthcare, the delivery of better outcomes and the holding down of costs to a minimum.

Reaching these goals, however, can only come about with the creation of new, more efficient operating models that are made feasible and affordable with the use of best practices such as the ones presented herein.