How to harness clinical and administrative data to aid processes
Schneck Medical Center provides an example of how data collected in clinical systems can be used to improve treatment of COPD.
Data creates a ripple effect. Every patient touch point in a hospital—from a routine vitals check to an imaging procedure to a diagnosis and treatment—contains dozens of related administrative and clinical data points that can reveal a lot about the quality and the cost of care that a patient receives.
For example, are patients diagnosed with a particular condition more or less likely to experience complications or readmissions? Are costs associated with certain procedures higher or lower than the average seen in peer group hospitals? Do patients admitted at a certain time of day or night end up costing more or less to treat?
While every hospital of every size produces this type of data, until recently few could ever hope to draw any meaningful insights from it. Now, that’s starting to change, with advanced analytics that give hospital administrators, quality departments and clinicians the information they need to quickly isolate common patterns in their patient populations to spot nascent trends, tailor more targeted interventions, and improve administrative efficiency.
Spurred by the value-based care movement, more healthcare providers have begun the process of realigning their administrative and clinical models to focus on delivering the highest possible quality at the lowest possible cost. Today, nearly two-thirds of all healthcare payments are based on value, research indicates. Along the way, key metrics such as 30-day readmission rates, bundled payment benchmarks and hospital-acquired infection rates have become the Holy Grails of hospital performance measurement.
As hospitals get better at tracking this data, it is now becoming possible to analyze the multitude of variables that are linked to each discreet data point to quickly and accurately identify avoidable care, spot gaps in the process of care, and even tailor interventions based on warning signs in that data.
The first step in leveraging analytics in the clinical setting is to accurately identify the biggest opportunities for performance improvement. To do this, hospitals will want to work with a large repository of de-identified patient discharge data that captures several years of system-wide patient care information and corresponding risk-adjusted data.
The critical component here is depth of information. Hospitals have access to an amazing array of data points, but often have a fairly myopic focus when it comes to overall performance. What’s even more important is the ability to develop a granular view of the process of care. To truly spot the kinds of trends and anomalies that can shine a spotlight on opportunities to improve care, it is important to know each step along the way that went into achieving that performance, good or bad.
For an example of how this can play out in a real-world setting, consider a recent case study involving a hospital quality improvement initiative focused on chronic obstructive pulmonary disease (COPD).
For Schneck Medical Center, a 93-bed community hospital in Jackson County, Indiana, where the COPD population is two times the national average, the disease was creating both clinical and operational challenges.
After analyzing its data, hospital administrators found that it had a raw readmission rate of nearly 14 percent and that 10 percent of those readmissions were because of COPD. Those COPD readmissions alone were costing the hospital nearly $300,000 per year.
Once the initial heavy lifting of data programming and analysis was completed and these red flags were identified, Schneck was able to start the process of adjusting its approach to COPD. This included developing a long-term care practice that makes weekly respiratory care visits, incorporates sleep studies into its observations, and conducts patient discharge planning. In addition, the hospital put in place new protocols that included the installation of a transition team to help with patient discharge, follow-ups with recently discharged patients, and annual facility education regarding COPD.
Over a three-year span of operational and clinical tweaks to its COPD process of care, the hospital saw its unplanned readmissions rate decrease by 80 percent. Even more impressive, the total number of COPD hospital admissions fell by 55 percent between 2014 and 2017. This last statistic is important because it shows that the efforts Schneck undertook to improve preventative care in its community—through interventions like weekly respiratory care visits and more active patient follow-ups—kept COPD patients from new hospitalizations.
As technology evolves, with high-powered analytics surfacing even more insights, the potential to realize results similar to Schneck Medical Center grows exponentially. With more and more organizations leveraging these powerful tools, the faster and more intuitively performance insights will emerge from seemingly disparate sets of data. That has the potential to help unlock huge value for organizations around the globe.
For example, are patients diagnosed with a particular condition more or less likely to experience complications or readmissions? Are costs associated with certain procedures higher or lower than the average seen in peer group hospitals? Do patients admitted at a certain time of day or night end up costing more or less to treat?
While every hospital of every size produces this type of data, until recently few could ever hope to draw any meaningful insights from it. Now, that’s starting to change, with advanced analytics that give hospital administrators, quality departments and clinicians the information they need to quickly isolate common patterns in their patient populations to spot nascent trends, tailor more targeted interventions, and improve administrative efficiency.
Spurred by the value-based care movement, more healthcare providers have begun the process of realigning their administrative and clinical models to focus on delivering the highest possible quality at the lowest possible cost. Today, nearly two-thirds of all healthcare payments are based on value, research indicates. Along the way, key metrics such as 30-day readmission rates, bundled payment benchmarks and hospital-acquired infection rates have become the Holy Grails of hospital performance measurement.
As hospitals get better at tracking this data, it is now becoming possible to analyze the multitude of variables that are linked to each discreet data point to quickly and accurately identify avoidable care, spot gaps in the process of care, and even tailor interventions based on warning signs in that data.
The first step in leveraging analytics in the clinical setting is to accurately identify the biggest opportunities for performance improvement. To do this, hospitals will want to work with a large repository of de-identified patient discharge data that captures several years of system-wide patient care information and corresponding risk-adjusted data.
The critical component here is depth of information. Hospitals have access to an amazing array of data points, but often have a fairly myopic focus when it comes to overall performance. What’s even more important is the ability to develop a granular view of the process of care. To truly spot the kinds of trends and anomalies that can shine a spotlight on opportunities to improve care, it is important to know each step along the way that went into achieving that performance, good or bad.
For an example of how this can play out in a real-world setting, consider a recent case study involving a hospital quality improvement initiative focused on chronic obstructive pulmonary disease (COPD).
For Schneck Medical Center, a 93-bed community hospital in Jackson County, Indiana, where the COPD population is two times the national average, the disease was creating both clinical and operational challenges.After analyzing its data, hospital administrators found that it had a raw readmission rate of nearly 14 percent and that 10 percent of those readmissions were because of COPD. Those COPD readmissions alone were costing the hospital nearly $300,000 per year.
Once the initial heavy lifting of data programming and analysis was completed and these red flags were identified, Schneck was able to start the process of adjusting its approach to COPD. This included developing a long-term care practice that makes weekly respiratory care visits, incorporates sleep studies into its observations, and conducts patient discharge planning. In addition, the hospital put in place new protocols that included the installation of a transition team to help with patient discharge, follow-ups with recently discharged patients, and annual facility education regarding COPD.
Over a three-year span of operational and clinical tweaks to its COPD process of care, the hospital saw its unplanned readmissions rate decrease by 80 percent. Even more impressive, the total number of COPD hospital admissions fell by 55 percent between 2014 and 2017. This last statistic is important because it shows that the efforts Schneck undertook to improve preventative care in its community—through interventions like weekly respiratory care visits and more active patient follow-ups—kept COPD patients from new hospitalizations.
As technology evolves, with high-powered analytics surfacing even more insights, the potential to realize results similar to Schneck Medical Center grows exponentially. With more and more organizations leveraging these powerful tools, the faster and more intuitively performance insights will emerge from seemingly disparate sets of data. That has the potential to help unlock huge value for organizations around the globe.
More for you
Loading data for hdm_tax_topic #better-outcomes...