Population health management can improve the care and well-being of a population. But it requires actionable analytics to successfully implement.
What is Population Health Management?
Population health management (PHM) is the process of improving healthcare for a population of individuals through greater care coordination and integrated, active engagement and outreach.
PHM was first introduced as a term in 2003, but it didn’t start gaining traction until around a decade later. As PHM continues to evolve, more healthcare organizations – which can include hospitals, clinics, physicians, and insurers – are adopting the practice.
How does Population Health Management use data?
A portfolio of technology solutions enables Population Health Management (PHM).
These solutions aggregate patient data across multiple resources and analyze it into a single, actionable patient record so organizations can clinically and financially improve:
- Care management
- Patient engagement
- Performance management
- Demand management
This means that the healthcare industry needs mature capabilities around data analytics and patient engagement technologies that enable population health.
Why is Population Health Management important?
With rates of serious health issues like cancer, heart disease, and diabetes on the rise in the U.S., it’s critical for healthcare organizations to ramp up quickly in PHM.
Ultimately, an advanced state of population health will need to factor in every single disease state and variable and make insights very actionable.
The key to building effective Population Health Management capabilities is actionable analytics.
3 Steps for Actionable Health Care Data & Analytics
- Look closely at how your organization approaches analytics
- Get the right tools to collect & manage your data
- Build a roadmap for successful alignment of patient care and IT
Look closely at how your organization approaches analytics
Analytics must be viewed across the entire organization. Most organizations understand they need analytics, but they also mistakenly believe they can start building all their capabilities off one of their core systems, such as an EHR system.
In reality, because population health is much broader in scope, multiple systems must be brought together in order to understand the dependencies and relationships between data sets.
For example, disparate critical administrative and financial data along with data from the organization’s ecosystem must be harvested and integrated into a data repository with patient records and more. Therefore, most organizations need help with where to house the data and how to structure it.
Once this data has been aggregated, analytics in PHM programs can be used in prescriptive and predictive fashions. For example:
- A patient with a history of heart failure and hypertension would be categorized as high-risk.
- If that patient has more than two hospital visits per year, predictive analytics would indicate a high probability that the person would be hospitalized within the next 12-month period.
- Prescriptive analytics might reveal that if the patient is likely to be hospitalized, she/he can be monitored daily at home and provided coaching regarding hypertension and diet to avoid hospitalization.
- This results in better patient care and cost savings.
Of course, this is just one example. There are various predictive risk models, such as the one outlined in Health Quality Ontario’s guide to HARP (Hospital Admission Risk Prediction).
With actionable analytics being a lynchpin to delivering PHM, an effective analytics strategy needs to leverage a long-term investment plan, technology and data assets, platform choice, data governance, build vs. buy decisions, and program delivery.
Get the right tools to collect & manage your data
Once you understand the inventory of the data you have to work with, you need to determine the right tools to extract and infer insights from them. What is the right technology to overlay on top of these data sets in order to start extracting and presenting information in a meaningful way?
This will highly depend on the size of your organization. For example, mid-to-large healthcare organizations might use:
- An SAP BI platform
Some organizations might use an off-the-shelf solution, such as:
- Crimson Population Health
- IBM Explorys EPM Suite
- Optum One Population Health
- Wellcentive Advance Outcomes Manager
- Verisk Health Population Health Analytics
Technology decisions will also rely on assessments involving current staff capabilities, organizational structure, technology infrastructure, and more.
Build a roadmap for successful alignment of patient care & IT
Every organization’s PHM roadmap will depend on the contracts between health delivery organizations and payers regarding various models built around patient types. For example,
- Managing patients with diabetes first
- Expanding capabilities to manage patients with both diabetes and hypertension
- Branching out further to include other disease states and combinations
The roadmap will define a portfolio of analytics applications that support each stage.
Note that organizations must be acutely aware of government regulations surrounding data capture and delivery.
HIPAA regulations must be critically regarded when physicians and their healthcare partners pull data from various sources, and they should be a mandatory element of any PHM roadmap.
Leveraging PHM and Data Analytics in Healthcare
Population Health Management is becoming imperative for healthcare organizations and will undoubtedly be a key driver in the evolution of healthcare in the next several years, with the goal of delivering enhanced patient care at reduced costs.
Organizations with mature PHM capabilities are already seeing these benefits.
Regardless of your present state in PHM, developing a comprehensive strategy and roadmap with insightful analytics and tool choices based on your organization’s specific needs and abilities is the first step to putting you on the right track for success.
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