By Shaillee Chopra, PMP, Senior Manager, GE Healthcare Camden Group
As the healthcare industry continues to make the shift towards value-based care models, organizations are faced with an increasing need to invest in technology platforms that offer analytics-based decision making. Faced with rapidly evolving strategic needs and surrounded by abundance of technology choices, healthcare executives struggle to conceptualize an effective approach to creating an analytics framework for their organization. Rather than adding one more product to the portfolio, organizations want to create an information framework that drives decisions and is action oriented.
Following are key strategic questions to ask when building an analytics platform for your organization. This approach ensures that you are able to successfully outline a data and analytics strategy that leverages adequate and accurate data across the network to create an action-oriented knowledge framework that is closely aligned with the value proposition.
1. What are the key problems you wish to solve?
Key to establishing an analytics framework that drives decision making and actions within the organization is to ensure that you start with the end in mind. What are some of the key objectives that your organization wants to achieve? Is facilitating transitions of care within the integrated network an immediate need? Is your organization aiming to capture market share by offering competitive services and offerings? Is referral management and reducing patient outmigration (often referred to as ‘patient leakage’) an area of concern? Is entering into value-based contracts one of the long-term goals for your organization? Succinctly outlining end goals guides what problems need to be solved for and helps conceptualize knowledge framework that will assist with the decision making.
2. What type of data is required to build this information framework and at what frequency?
Value-based goals and metrics often require harmonized data across the continuum. Atypical data types include, but are not limited to, billing data, inpatient EMR data, outpatient EMR data, ambulatory data from employed and affiliate provider practices, claims data from payors, HIE data, external pharmacies data, bedside monitoring, and at-home monitoring data.
Outline which data types are required to be updated in real time to support predictive analytics needs (clinical data elements such as diagnosis, problems, medications, etc.) and which ones can be brought in a retrospective format (claims, utilization etc.). This will assist you in establishing an integration architecture with various source systems across the network. Developing integration architecture also offers an opportunity to estimate realistic answers to how much the infrastructure will cost and how long will it take.
Consider building a data integration roadmap that outlines the sequence in which various data domains would be aggregated into the analytics platform.
3. What is the data worth?
The relative importance of data from varied locations across a continuum of an integrated care network can influence your data acquisition strategy. Key considerations include:
- Data relevance. How relevant is the data type in your decision making process and what problems does it help solve for?
- Data accuracy. Are all expected attributes of data accessible and transmittable? Is there minimal uncertainty due to workflow at the front lines?
- Data frequency. Is the data available in a required format (discrete and structured vs. static image)? Is the data transmittable at a desired pace (updates to lab data might be required in real time to facilitate transitions of care; while claims data can be loaded in retrospectively to produce trends in cost-of-care)?
- Data depth. Is there needed depth of historical data that is aligned with analytic needs (certain specialties such as oncology clinical historical data needs go farther back than primary care)?
- Data consistency. Are the standardized vocabularies integrated into data domain offering consistent definition and interpretation of each data element?
4. What organizational capabilities need to be developed to support the future state?
It is important to identify key consumers of analytics within your organization early on. Understanding key information needs of the users, level of data literacy (ability to understand and interpret data) and the ability to exploit information offered via an analytics platform determines the pace at which your organization can adopt a knowledge-based decision making system.
Consider setting up a multidisciplinary data governance council that aims to provide:
- Guidelines for management of the quality of data being leveraged across the continuum
- Data literacy within consumers of analytics across the continuum
- An operational framework that allows for maximizing data exploitation for the organization’s benefit
5. What are some key requirements for technology solutions that will aggregate and harmonize this data?
Product selection driven by clearly outlined end goals that the organization wants to achieve and key functional capabilities it wants to enable (care coordination, consumer engagement, increase market share, stop leakage) ensures that technology is successfully positioned as an enabler of operational workflows.
Key considerations when outlining requirements for an aggregation platform include:
- Capabilities enablement. How does the technology platform operationalize key functional areas for the organization?
- Product functionality. What are some of the key functionality needs to support operational objectives for the organization?
- Data consumption abilities. What is the data footprint that the product can consume (clinical, financial, socio-demographic etc.)? What is the integration footprint with key healthcare technology vendors?
- Product roadmap. What is the product roadmap, and how does it align with your organization’s strategic goals?
- Speed to implementation. What is the product implementation methodology? What are the key resource needs from your organization, and what are key external dependencies that impact speed to market?
Transitioning to outcomes-based decision-making frameworks enables your healthcare organization to harness the power actionable analytics. You can leverage these best practice recommendations to avoid commonly observed pitfalls and implement a sustainable and scalable solution.
Ms. Chopra is a senior manager with GE Healthcare Camden Group and specializes in developing and managing innovative technology portfolios for value-based and clinically integrated healthcare networks. She is highly experienced in leading information technology and consumer experience strategy development, as well as transformations to enable clinical integration, accountable care, and population health management strategies for organizations invested in innovation and transformation of care delivery models. She may be reached at firstname.lastname@example.org.