In the United States, one percent of healthcare users account for 21.4 percent of total U.S. 医疗保健支出. “Frequent flyers” to emergency departments (EDs) heap a considerable cost burden onto the system, especially considering that reducing unnecessary ED visits could save an estimated $38 billion.
Despite the huge expense of unnecessary emergency department utilization, there is a silver lining – these encounters produce valuable data that can ultimately be used to curb healthcare costs. With every mouse click and keystroke, 电子医疗纪录 are generating discrete data points that can help physicians and system leaders better understand the drivers of utilization in the communities they serve.
As hospitals and health systems prepare for Stage 2 of meaningful use, they have the opportunity to employ data analytics in building and enhancing their business intelligence capabilities.
For a system planning for population health, the nature of ED care creates a data-rich environment that offers insight into the health issues (such as the flu) affecting a large and diverse population. The organization can also capture a snapshot of the most acute patients in the region, such as chronically ill patients, who routinely use the ED for conditions that could be effectively treated in the primary care setting.
Predictive analytic tools can be used to glean critical information from the encounters and patient records of high-frequency users. 通过系统地 sifting through the data, software can help clinicians discover and flag risk factors for repeat admission and use this information to design targeted triage and follow-up care protocols geared toward reducing utilization. 另外, collecting and analyzing this data can reveal opportunities to fill gaps along the healthcare continuum.
例如, in the example below, leaders in a busy east coast-based ED recognized an uptick in re-admissions and were looking for ways to identify high-risk patients at the beginning of their visit. After discussions with physicians and clinical staff, analysts collected and examined data from clinical and financial systems.
What resulted was a decision tree that could identify patients at high risk for readmission and trigger the appropriate follow-up care protocol at triage.
然而, simply purchasing software is not enough. To realize the full potential of predictive analytics in the ED, health systems must be prepared to create a culture that embraces a data-driven approach 操作.
Specifically, organizations must be prepared to:
- 拥抱电子病历 – For many providers and their clinical teams, effective implementation of 电子医疗纪录 requires a change in “how we’ve always done things.” Like any change, it might not be immediately welcomed. 然而, in order for many analytic tools to be effective, using the EHR as it is designed is essential. Working around the EHR limits the efficacy of any analytics platform.
- Validate data integrity – Missed key strokes and typos can frustrate English teachers and editors, 但在急诊室里, they can significantly affect care quality. Department leaders must make high-quality documentation a priority. Tracking deficient charts and following up with providers and clinical staff is a mandatory competency.
- Fit the tool to the problem – While many analytic platforms may offer solutions out of the box, it’s important to first understand exactly what problems exist. Leadership and physicians need to work together and target areas of opportunity before choosing their analytical approach.
EDs are gold mines for rich, actionable data – data that offers organizations an inside view of the health behaviors of patients and communities. Developing competencies in business intelligence and predictive analytics will allow progressive hospitals and health systems to leverage this data in responding to and shaping utilization patterns to ensure that patients are seen in the right setting by the right provider at the right time.
This post was originally featured on the athenahealth Health Care Leadership Forum – September 19th, 2014.