Healthcare Analytics Can Improve Financial Performance, Efficiency and the Patient Experience in Healthcare
VIE Healthcare Performs Data Analytics in Healthcare so That Healthcare Providers Can Improve the Way They Manage Chronic Conditions, Improve the Efficiencies of Their Hospital Systems, Optimize Their Costs and Improve Their Revenue.
Data analytics initiatives have the potential to transform the healthcare industry in multiple areas, but the sector has been slow to tap into its potential. Even when health care clinicians regularly apply actionable insights or account for market trends, many providers don’t adequately leverage big data analytics. With large swaths of electronic medical records, patient data, and clinical data available to health care brands, you must be using data analysis and EHRs to provide better care. When you couple big data analytics with data mining, machine learning, artificial intelligence, and other health data technology, it’s that much easier to optimize costs and explore healthcare data insights.
For U.S. health care companies, understanding what spend data analytics in healthcare means can encourage more hospitals to embrace its benefits. With this in mind, it’s easier for a health data analyst to use significant data insights to reinvest in healthcare delivery, technological innovation, and patient care standards. From there, you can transform data, analyze timely information, and use algorithms to parse electronic health records (EHRs) and other clinical data.
The VIE Healthcare Advantage -
The Only Comprehensive Cost Savings Solution™
Forensic Line-Item Historical Analysis
Detailed Contract Dissection
Categorization & Benchmarking
Savings Opportunity Identification
Manage Contract Performance
Can Your Hospital Benefit From Data Analytics?
- Patient care management: The first step lies in improving the level of care provided to a patient, leading to lower healthcare costs and better outcomes. Care management requires applying big data and advanced analytics to identify groups of patients with specific characteristics who are at risk for poor outcomes and then changing the standard of care accordingly. For example, driving lifestyle changes can lead to better patient outcomes with chronic conditions and cardiovascular disease. In addition, each lifestyle change leads to lower costs associated with initial treatment and maintenance and a higher quality of life. These caregiver quality improvement opportunities are critical for cost containment, especially amongst physician groups that operate within highly competitive local markets across the United States.
- Evaluate patient satisfaction: Measuring how satisfied patients are with their care using such measures as patient surveys and social media engagement data. Adding analytics to these traditional qualitative tools allows for personalization of care based on feedback from the patient population. Practitioners can employ healthcare analysts to review patient workflows, spot areas of opportunity, and mitigate ongoing patient risk factors. Predictive analytics and health informatics are critical within the domain of patient satisfaction. With health data analytics, it’s easier to verify patient engagement.
- Patient Cost Efficiency: Identifying how much each patient costs a hospital, as well as a cohort of patients. Identifying costs allows for more targeted cost management of the overall patient population, leading to improved patient outcomes and a balance of cost optimization. Whether you’re treating opioid abuse, high blood pressure, or a high-risk condition like asthma, it’s essential to know how much different services and medical treatments cost your health care system or emergency department.
- Perform Utilization Analytics: Measuring the level of utilization of a particular resource or service. This type of analytics helps to assess the productivity (or inefficiency) of different departments and physicians. When you pair these health data analytics with patient records, you’re able to paint a holistic picture of your healthcare organization’s performance, which can also help with strategic planning and departmental structuring.
- Providing proactive care utilization analysis: Healthcare business analysts need ways of tracking the patients who have used a service or resource and how it affected their outcomes. Whether you’re monitoring illnesses, heart rate information, or other public health data, it’s essential to make a personal connection between care delivery and healthcare analyst tools. In this way, patients can be tracked and targeted for services that would lead to better outcomes (i.e., disease management programs for those at risk of diabetes). In addition, this provides health organizations with greater flexibility to tackle patient needs at an early stage and help address readmission rates.
- Ad hoc reporting: Healthcare managers need more than temporary access to new insights and reporting, especially true when grappling with complex problems, implementing best practices, and providing data to key stakeholders in response to specific requests rather than as part of an ongoing process or in real-time. This is useful when trying to quickly analyze data for a particular request (and in healthcare, there are many unique data requests, including readmission data, methodology correlations, lab tests, biostatistics, and much more).
- Population Health Data Analytics: We analyze population health data to improve the health and wellbeing of a population. An example would be using data analytics to assess people’s health in a specific hospital and then develop disease management programs around that information. Another example would be analyzing the data based on age, sex, race, etc., to uncover patterns that may help with targeted research or care management. Coupled with predictive analytics tools and it’s easier to bolster your brand’s clinical decision support and invest in innovations that factor in your demographic information and cost of care data.
- Financial Analytics: Financial modeling can work with statistical and mathematical models to predict future financial needs and optimize existing budgets. These models can also give a more in-depth look at the overall cost structure of a hospital. This provides holistic information from a variety of sources. Analytics experts can review administrator salaries, medical device spending (smart devices in clinics, prescriptive analytics platforms), and contracts (government agencies, i.e.) for data trends and new ways to address anomalies. This helps you review better manage your payers, vendors, distributors, and supply chain.
- Disease Management: Grouping patients based on shared disease characteristics and then targeting them with intervention programs can help improve their outcomes and lower costs associated with long-term health problems like chronic conditions. Big data analytics can empower intervention programs and use machine learning tools to help streamline disease management, track hospital admission, and review patient engagement during office visits, appointments, emergency department treatments.
- Provide Spend Data Analytics: Giving a comprehensive, real-time view of a health care system’s spend on supplies and services. This helps identify cost savings opportunities and place high-cost product lines that need consolidation or a new pricing structure. In addition, this empowers healthcare professionals to make more effective workflow decisions regarding spend data which can impact every aspect of your business, from your supply chain and distributors to your network administrator specialists.
- Provide Cost Benchmarking: Comparing costs between providers, hospitals, and healthcare systems to identify areas where charges occur unnecessarily. This is done by looking at historical purchasing patterns and comparing them with expected costs based on the population served. Cost benchmarking also helps health professionals apply prediction model data to forecast trends and compare financial performance.
- Managed Care Contracting Analytics: Analyzing trends in managed care contracting, for example, measuring how much a health plan is spending on a particular provider or hospital. By evaluating trends over time, it is possible to identify areas where increased spending is due to changes in the population being served rather than inefficient practices by the provider itself.