5 Vital Requirements for Actionable Healthcare Analytics
As the healthcare ecosystem you practice within gets more complicated, it becomes harder to keep up and see how all the changes in healthcare analytics, predictive modelling, and big-data can benefit you, your patients, and organization. Interoperability in healthcare demands complete access to essential clinical, diagnostic and financial data that is collected and standardized to deliver coordinated and value-based care to patient populations.
There’s a huge need for healthcare analytics, due to rising costs in United States. As a McKinsey report states, “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.”
Laboratories, Health Systems, Physician Practices, Payers, Imaging Centers, Health Information Exchanges, Accountable Care Organizations and other Healthcare Organizations are motivated to access significant amounts of aggregated clinical and financial data to improve population health and disease management. Comprehensive and actionable clinical and financial data can improve population health, assist in clinical research and decrease costs.
Without timely and complete historical and current lab and diagnostic test results, clinical decision making can be decelerated impacting patient outcomes, quality scores and payment for performance. Without aggregated and meaningful data payers may fail to identify possible risk members that should be enrolled in their disease management programs.
The benefits of complete, timely and aggregated lab and diagnostic data helps in using predictive modelling for early identification of disease for enhanced population health, determine suitable treatment plan, monitor ordering data and create workflows in support of referrals management. Healthcare organizations can streamline operational workflow while improve overall communication.
Providers depend on data to take informed decisions at the point-of-care and help in accelerating patients’ health. But this data may be unworthy if providers are not able to use it on a real time basis. Data related analysis and connectivity need to be simple, relevant, and easily available to assist providers personalize and plan better for patient care. Relevant notifications and alerts filtered by priority help health organizations to make right decisions at the right time.
Healthcare data is vital for providing superior-quality clinical care. There are certain attributes of effective and usable analytics. Most important five requirements which propel the medical decision making and help in improving quality outcomes are listed below:
- Real-time analytics
For providers to use analytics in a meaningful way real time analytics is critical to decision making steps during a patient encounter or at bedside. Though historical data analytics is important from research and referencing perspective, real time analytics will help providers to utilize the window of opportunity available during a patient encounter, take proactive steps to prevent potential risks. IoT and Wearables can collect patients’ health data continuously and send this data to the cloud. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions. Organizations and care managers can use real time analytics to monitor this enormous data stream and react every time the results need attention. For example, if a patient’s blood pressure increases alarmingly, the system will send an alert in real time to the caregiver who will then act to reach the patient and administer measures to lower the pressure.
2. Quality of information
Information provided to healthcare organization in form of analytics need to be complete and actionable to enable accurate insights for caregivers. Challenges of data retrieval from different electronic health records, and data mining need to be met before analytics tool is utilized. Improving data entry methods, saving care giver’s time in documentation are some solutions to meet this challenge. If the data is valid, caregivers will use analytics for their patient care planning without giving a second thought. Combination of graphics, text for displaying analytics helps the provider to get the complete data picture and take actions.
Tables, graphs, control charts with text descriptions to display opportunities and recommendations will help organizations in improving healthcare outcomes. Data represented visually is easy to represent and understand information quickly especially when data is complex or new to the caregiver. Identifying problems and solving them is a key role of analytics.
4. Evidence-based care suggestions
Disease information assimilated through multiple resources provides caregivers superior insight into the patient’s disease status. Such information is utilized to create patient care recommendations and it helps providers from clinical support. Analytics which can predict risks of any event for a condition is highly valuable irrespective of their decision to retain or change the treatment plan as it improves the awareness of risks among provider and patient. Quality analytics combined with evidence-based recommendations improves the clinical workflow and provides value at the highest level.
5. Caregiver Engagement
Providing value with inclusion of analytical tools and solutions helps caregivers to adopt proactively. Including them in the analytic program building process helps the final product and solutions to be closer to clinical context of meaning and actions. Hence involving providers at all levels of development and enhancements ensures that the product is trusted and used effectively. “Involving clinicians in meaningful ways from the beginning of the development process through to the deployment phase ensures the final product is trusted by the clinicians who will use it,” says Daniel Exley, vice president of information services at MemorialCare Health System in Fountain Valley, Calif. “Putting timely, trusted analytics into the hands of physicians and their care team partners enables fact-based decision making to improve quality, reduce waste and improve the patient experience.”
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