Healthcare is transitioning from a cure based fee-for-service model to preventive value-based model designed to deliver the best quality care at the lowest possible price. Healthcare organizations are adopting value based methods to provide extensive care management to patients. Care management is the range of activities conducted with the intention to improve a patient’s quality of life and lower the need for healthcare services by helping both the patients and the caregivers. The real challenge lies in scaling up existing systems, to make data driven decisions for identifying and coordinating with the target patient population, and measuring the effectiveness of the interventions.
Rapidly expanding field of advanced analytics has begun to address this gap, and it is playing a significant role in the growth and evolution of care management. Predictive analytics driven care management systems will address these issues by streamlining workflows, prioritizing daily tasks for care givers, and steering activities to the areas that will positively impact the patient lives.
To cite some examples, to implement a Rapid Recovery Pathway for cardiac surgery patients, Mayo clinic deployed advanced analytics based decision-support tool in their facility based on the new models of postoperative care. This solution helped predict outcomes of the patients better and have improvements in their care, resulting in a reduction of cost per case by 7% and a reduction in length of stay by 1.5 days (1). Similarly, The University of Pittsburg Medical College (UPMC) has leveraged advanced analytics to improve their clinical and financial decision making. With their home-grown Enterprise Analytics platform they were able to slash the readmission rates by 37%. (2)
However, many challenges still remain for large scale adoption and implementation of Analytics in care management.
Care Management System:
It comprises of a suite of tools that integrates data from all possible sources, stratifies patient’s risk, organizes and manages patient record and provides two-way communication with the caregivers. An efficient care management system has analytics at its core, which enables the system to leverage data to determine trends and patterns that drive better outcomes for patients, and change the day-to-day workflow for clinicians/medical practitioners.
Core Competencies of Care (3)
Depicted are the core pillars of care management, starting from the data integration across multiple data sources, followed by analytics-driven patient stratification on the aggregated data for better care coordination between care team on patient’s care planning. Ensuring that the patients are informed throughout the process for improved patient engagement, and finally bringing in advanced reporting capabilities for Performance evaluation on the effectiveness of care and thereby enabling better patient outcomes.
Benefits of analytics in care management:
Analytics will help organizations to achieve the care management goals by providing risk stratification for high risk patient identification. This will enable the care manager to assign an appropriate care plan to the patient leading to better Care coordination during transition from hospital based care to home care of care. To cite an example, Owing to the rising healthcare cost of patient with complex needs, Kaiser Permanente wanted to better characterize the highest-risk segment of the population and to redesign care to meet their needs. They leveraged advanced analytics to predict who will be their high-cost patients (Top 1% of patients costing 29% of the total health care costs, i.e. $4Billion) next year and provide focused care to them (4).
Our view of Predictive analytics based Care Management
After care plan assignment, Analytics will empower medical practitioners and insurers to measure the improvement in patient’s condition by comparing the patient outcomes and medical utilization data after the care program onset with the prior historical data. This would enable hospitals to evaluate and create clinical evidence, on how high-risk chronic patients are responding to care management. From the payer perspective, they can leverage this information for value based reimbursement to the hospital providers. Patients can in-turn use this to keep track of their own individual health.
By adding and tuning this additional information to predictive models, caregivers can increase the efficiency of care management programs. In the future, medical practitioners and caregivers with the help of analytics can access the individual’s needs, evaluate it, determine the services he/she is eligible for, implement care plans, provide the best quality services, and monitor or re-assess the needs. Patient-centric healthcare for the masses will be possible with data analytics. Integration of analytics in care management will also address the significant challenges for medical care globally such as the rising high cost, low quality, fragmented delivery, limited resources.
Data Security Challenges in implementing Analytics driven care management:
With rising cybersecurity crimes and data-thefts, patients can be unwilling to adopt care management systems with analytics for fear of privacy intervention. The data to be used in care management should be secure, accurate, including complete history and properly formatted. The integration of analytics in care management particularly in emerging economies will be difficult due to lack of adequate IT infrastructure.
We see analytics to become the pivotal part of care management in near future, aiding the physicians to effectively identify patients who are most likely to benefit from care program, make informed decisions and manage their care to improve outcomes and lower costs. Similarly, care management analytics also assists payers in successfully implementing and managing the value-based payment models.
That being said, all associated stakeholders like (Providers, Payer, Patient and Care manager) should coordinate effectively, to facilitate better integration of patient’s Clinical, Financial and Demographic data needed for the analytics to provide better Patient care and thereby making the entire ecosystem to succeed with the Outcomes-Based Contracts.