Data analytics tools within IT systems should address cost containment and efficiency issues in the healthcare industry. Alas, too many shortcomings keep them from being used at their full capacity.
Romain Doutriaux, solution marketing manager at Dataiku in New York City: Patient no-shows are an appropriate example of this, as they impact all healthcare providers. A recent Medical Group Management Association study found even well-run practices have a daily average of 12 percent no-shows and last-minute cancellations.
Many tactics have been employed to decrease no-show rates, but none of them address the fundamental no-show issue: How can a provider avoid scheduling someone for a time slot that has a high likelihood of being missed?
In theory, providers could compare patient appointment datasets with data from external sources, such as weather, transportation schedules and holidays, to infer where problems with appointment commitments arise. But in practice, the no-show issue remains a painful one that health IT systems have not effectively solved.
Fragmentation in the U.S. healthcare IT system is a fact. Everyone is aware that EHR users struggle with different standards. Interoperability is a persistent challenge in health IT and it appears analytics is no exception. With 72 percent of healthcare providers using more than 10 electronic interfaces to collect data, there is not much one can do to properly leverage data analytics.
Here are a few tips to help overcome this major bottleneck:
• Develop IT interoperability. Do not commit to any proprietary systems, ever. Make sure your datasets are interoperable. If not, an alternative option is to use collaborative data analytics software. Effective software should be able to connect datasets from disparate IT systems to generate clear business outputs.
• Get prospective. This step enables providers to move from retrospective summary analyses that reveal problems that have already happened to prospective analyses that enable the anticipation and mitigation of problems ahead. Unlike traditional dashboards and retrospective analysis that are high-level visual summaries, prospective analysis is highly focused on a single, quantifiable business problem.
• Define your goals. You are not breaking down your IT system's barriers just for fun. Being able to aggregate your various datasets gives you the power to improve efficiency, physician profiling, staff optimization, fraud detection, disease management, etc. A few datasets and proper data analytics software could help providers save more than 20 percent on operating costs. What are you waiting for?
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