Electronic health record (EHR) technology has become truly transformative for the healthcare industry; prepared or not, healthcare teams are increasingly relying on new information technologies to improve the delivery and management of care. EHRs have enabled faster and easier access to patient information, and hold the promises of improved workflows, efficient sharing of information across communities and reduced costs for many physicians and hospitals.
But now that nearly 80 percent of physician practices in the U.S. today have EHR systems in place and the Centers for Medicare & Medicaid Services’ (CMS) meaningful use program is well underway, it is time to look to the next stage of health care technology and innovation. Health care teams must now move beyond the first step of digitizing patient records to transforming this valuable data into meaningful and actionable knowledge that will help care teams make more informed decisions at the point of care and ultimately, improve outcomes.
For this impact to take place at both the individual level and at the population level, care teams need to leverage clinical analytics that will provide visibility into important clinical trends across the entire population. For example, being able to review trends in diabetes care or readmission rates across a population represents an opportunity for specific, meaningful change to improve care delivery and outcomes.
For a practicing clinician, “population health management” means being able to see where an individual patient is within the clinician’s or clinic’s population (e.g., whether the individual’s chronic condition is above or below population benchmarks) and to take action at the point of care, as well as being able to refer to relevant population health metrics.
For a patient, clinical analytics presumes trust, not only in the competency and care of the physician, but also in the security of his or her information. Population health management and analytics tools must ensure that patient information can be gathered, stored, and used in a way that is demonstrably secure.
Care teams should consider four key elements when exploring clinical analytics tools for population health management:
Guest post by: Sai Subramaniam, Ph.D., Business Head, Life Sciences & Healthcare at Persistent Systems
According to a recent report only 16 percent of hospitals have clinical decision support capabilities, but IT leaders call it a top priority for the next 12 months. Healthcare reform is all about achieving better quality care at lower costs, and clinical analytics is integral in delivering on this promise. For example, reducing 30-day r-eadmissions and hospital-acquired infections alone is expected to save more than $25 billion dollars in the healthcare system. Analytics on integrated claims and clinical data will allow health systems to pinpoint effective clinical and operational interventions. Here are five high-impact outcomes that health systems can achieve using clinical analytics.
30-day Re-admission Avoidance: Hospital re-admission rates are high for patients whether they are in Medicare, Medicaid or Private insurance plans. People with multiple chronic conditions and mental health conditions are at an increased risk of re-hospitalization because of inadequate care at discharge. Demographic and social factors also dictate if the care transition will be effective or not. Evidence-based rules allow stratification of patients based on these factors. This allows caregivers to give more attention to high-risk patients during hospital discharge.
Enhanced Surveillance and Preventive Care: Growing evidence suggests that education and health coaching will facilitate behavior change and achieve cost savings. The population in the program needs to be screened and stratified to identify at-risk patients. Predictive modeling and business rules can help to identify individuals who may not be diagnosed but have relatively high risk of developing diabetes in the future. Similarly, a cancer surveillance model based on linking environmental, genetic, and lifestyle factors can be used. This will allow early interventions and proactive follow-up care.
Improved Medication Adherence: Non-adherence is said to be responsible for more than 10 percent of hospital admissions and 40 percent of nursing home admissions. Patients on average don’t fill more than 25 percent of new prescriptions. Costs because of lack of medication adherence exceeds $100 billion. Predictive analytics on patients’ past prescription claims data will allow the health system to create an adherence score, and facilitate a proactive approach to managing compliance.
Unplanned Admission Avoidance: It’s important for health systems to identify patients with chronic conditions who may be at risk of emergency hospitalizations. For example, studies suggest that people with respiratory and cardiac comorbidities, with higher hospital utilization in prior years, have a higher probability of hospital admission. Determination of such factors along with socio-demographic characteristics, will allow application of predictive models to identify people at-risk.
Length of Stay Performance Management: Several factors impact the patient’s length of stay in the hospital. This includes demographic as well as hospital operational characteristics. There are standards for length of stay based on diagnosis related group and clinical disease factors. By comparing this with patient profiles, providers can utilize resources efficiently to provide optimal patient care. This will result in significant cost savings as better case management should help to reduce the average length of stay.
Dr. Sai Subramaniam is the Vice-President of Persistent Systems’ Life Sciences & Healthcare business. In this role, Sai is responsible for the overall business growth of Healthcare & Life Sciences business segments.
