According to Wikipedia, Healthcare analytics involves “data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs)), and patient behavior and sentiment data.” This area of healthcare is growing rapidly in the United and focuses on clinical analysis, financial analysis, supply chain analysis, as well as marketing, fraud and HR analysis. In essence, healthcare analytics looks for patterns in healthcare data that can inform ways for care and outcomes to be improved at the same time that excessive healthcare costs can be controlled.
Healthcare analytics has the potential to improve healthcare in ways we are only beginning to realize. Here are some of them that are already becoming reality:
CIO Magazine describes how “Kaiser Permanente is reducing patient waiting times and the amount of time hospital leaders spend manually preparing data for operational activities using a combination of analytics, machine learning, and AI. The healthcare consortium's Operations Watch List (OWL), developed as part of its "Insight Driven" program, is a mobile app that provides a comprehensive, near real-time view of key hospital quality, safety, and throughput metrics, including hospital census, bed demand and availability, and patient discharges.” This app uses data from the organization’s electronic medical record to power leadership decision-making. Some of the benefits already identified are reduced admission wait times, speedier emergency department responses, and far less time spent manually preparing data.
Using Predictive Analytics to Reduce Hospitalizations
The same CIO article offers that the Emergency Department at Chicago’s Northshore University HealthSystem is using data and analytics to assess patients suffering chest pain, to help determine who needs to be admitted for observation and who can be sent home safely. Some of the benefits of this approach are shorter wait times, more free beds for those who really need them, lower costs, and more efficient use of staff time. According to CIO, “NorthShore CIO Steve Smith says it has reduced the Chest Pain Observation Days rate by 10 percent without increasing the rate of ED returns, mortality, or morbidity.”
Improving Care While Reducing Costs
Miami’s Jackson HealthSystem (JHS) is using a data integration engine to focus on improving care and controlling costs. CIO shares how the organization identified “’high utilizers’ of JHS's emergency department (ED). High utilizers are patients, typically underfunded, who use the ED for primary care, often because they aren't aware of other options.” The system alerts providers when ED high utilizers enter one of the JHS Eds and “provide information about the patient, what service line they're in, where they're currently located, and what's currently being done with them. Case managers can then meet with them and help them get into a more appropriate service line.”
Limiting Intensive Care Stays
Philadelphia’s Penn Medicine was already focused on getting ICU patients off ventilators before the COVID-19 pandemic made it a priority. The CIO article cited earlier describes the organization’s data driven dashboard designed “to alert respiratory and nursing staff when interventions were needed and when patients might be ready to be weaned from ventilators.” The article offers that the “application has helped Penn Medicine reduce the time patients spend on a mechanical ventilator by more than 24 hours.”
Improving Collaborative Data Exchanges
An article from Fierce Healthcare tells us that the COVID-19 pandemic has inspired “typically competitive markets [to] come together to mobilize and exchange data quickly.” In a specific example, “Rush Medical and neighboring Chicago acute care centers are collaborating to share bed capacity data. This partnership has added reaction time for both case and emergency management to mitigate ambulance diversions and inform on-the-ground staff of abnormal fluctuation in patient flows.”
Enhancing Cross-Functional Cooperation
Fierce Healthcare also relates that “A few organizations have begun to oversee collaboration among emergency management and patient safety teams to open their doors safely and efficiently to non-COVID-19 patients. These partnerships have created innovative strategies like using emerging technologies units to repurpose video camera analysis (which is often utilized for retail analytics) to now ensure physical distancing is enforced and at-risk populations remain safe on large campuses.”
HealthStream’s vision is to improve patient outcomes through the development of healthcare organizations' greatest asset: their people. That’s why we are working to make it possible for healthcare organizations to make organizational decisions that are informed by healthcare workforce data analytics. Human Resources maintains huge amounts of people data, and this data is critical to business operations. Some healthcare organizations are utilizing predictive analysis to increase their overall ability to predict attrition risks, to select high performance job applicants, analyze engagement, understand employee productivity and performance, determine leadership training needs and potential, and manage staffing needs based on leave of absence trends.
HealthStream commitment to analytics-driven workforce development in healthcare is embodied by our flagship nursing workforce development solution, jane™, the World’s First Digital Mentor for Nurses. JaneTM harnesses the power of artificial intelligence (AI) to create a system that personalizes competency development at scale, quickly identifies risk and opportunity, and improves quality outcomes by focusing on critical thinking. Leveraging decades of research and with over 4 million assessments completed, JaneTM was designed to power lifelong, professional growth of clinical professionals. JaneTM is an important component of HealthStream’s suite of clinical development solutions.
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