Embracing an agile approach to analytics drives market competitiveness
This article was first published in Health Data Management
With the healthcare payer and provider markets consolidating, interoperability regulations taking effect and the increased adoption of digital health, there is more data available in healthcare than ever before. This data holds insights on how healthcare organizations can revolutionize the way people interact with their healthcare. Data analytics can help improve the way providers detect disease and monitor progression. And it can assist in determining how to prevent adverse health events.
But many healthcare organizations face paralysis when trying to capitalize on this opportunity. Fragmented data that makes it difficult to find true insights, conflicting and often divergent strategic priorities, and siloed functional teams all hamper organizations’ ability to move with purpose and pace.
So how can healthcare organizations leverage the rich and growing pool of data to make the right strategic decisions and drive competitiveness in the market?
Embracing the ‘agile’ mindset
Embracing the “agile” mindset in healthcare analytics enables organizations to address data challenges using existing data in addition to exploring more complex hypotheses as further data or hypotheses reveal themselves. An agile mindset helps organizations pivot when needed, resulting in less time lost and lower investment costs.
Based on our experience working with healthcare payer and provider organizations, this opportunity can be achieved through the adoption of three core approaches.
1. Build genuine and unanimous leadership team commitment
With data sets rapidly growing, healthcare organizations need a clear direction for data strategy and governance.
The insights to be gained from these data sets are potentially limitless, which can slow any progress because of the intimidating scale of the work that could be done. Healthcare organizations need to recognize the value and prioritize attaining incremental insights.
For example, one healthcare organization in the Northeast is using data insights derived from social determinants of health and other key data sets to identify increased care management opportunities within subsets of the population to provide the right care at the right time. With unanimous support from the leadership team for an agile approach, this organization has seen significant growth through new, data-driven opportunities, specifically within their value-based care payment models compared with fee-for-service models. Using a variety of data points, this organization was able to reduce the cost through avoidance of surgical interventions for a musculoskeletal diagnosis.
Given the significant cultural implications, it’s essential that senior leaders lead by example and manage their teams to adopt new practices throughout the change. Leaders’ commitment to how to use agility to achieve outcomes must be unanimous, unshakeable and tangible.
Although it’s likely to require significant time and capital investment to wean staff members from reliance on governance-heavy processes, the agile approach can generate many ideas for improving customer service using shorter feedback loops.
2. Create the conditions for success from the outset
The three conditions for embracing agility are cultural curiosity, data-driven decision-making and cross-functional teams.
The use cases for healthcare analytics are growing as organizations move deeper into population health management and accountable care and consumers demand cost-effective services.
Leaders often underestimate the need for their organization to be culturally curious and open to new ideas. Finding data insights is about more than executing algorithms; it also involves embracing an experimental mindset, where failure is a large part of discovery.
While organizations typically welcome new ideas, the stigma of failure often hinders individuals from experimenting with new ideas, which only serves as a path to creative stagnation. Nurturing cultural curiosity and the inevitable failure that comes with it is therefore vital as agility becomes part of your everyday organizational practices, embracing a culture of “fail fast, fail forward.”
Qualifying or disqualifying hypotheses early on allows teams to fail fast, learn and move on.
Additionally, insights gleaned from data analytics can provide evidence for new hypotheses, accelerating the process the more it’s exercised. It’s imperative that objectivity reigns supreme when evaluating hypotheses to capitalize on the value of “failing fast” and to ensure that decisions are being made for the right reasons.
Being scientific in decision-making upfront not only sets organizations up more readily for commercial success, but it also reduces – if not neutralizes – the “loudest voice in the room” factor.
The optimal team set-up to embrace agility is a cross-functional approach. This means breaking down functional silos that may exist and organizing team members around the streams of value that customers consume.
For example, a West Coast-based payer and provider organization was embarking on a transformation of its finance division to drive greater standardization in their business processes. Using a data-driven process, a cross-functional project team worked to ensure there was clarity about business process roles and responsibilities. The team trained certain staff members on process standardization and optimized the finance processes through standardization of key roles and responsibilities. As a result, the organization was able to drive streamlined processes with enhanced workflows, approvals and visibility across teams regarding recharge agreements. Similarly, the team was able to drive greater consistency in how they perform their work to support the business case process.
While change can be challenging, the long-term benefits far outweigh the initial investment. Those benefits include quicker implementation of new ideas and new products and a structure to further unlock staff members’ creativity. Although the hypotheses being tested and developed are likely to be narrower in scope, the expediency with which they can be brought to market will have more incremental customer value and financial return.
3. Be laser focused in uncovering insights
When striving to make sense of huge data sets, there’s a temptation to tackle everything at once and pursue every available opportunity.
This approach risks taking on too much simultaneously and bringing comparatively little to fruition. It also fails to take advantage of the benefits of short feedback cycles that enable incremental delivery of results.
Healthcare data insights are becoming the foundation for precision medicine and actionable pathways toward more efficient development of targeted treatments. And this requires a focused approach. Insights driven by growing amounts of data enable clinicians to make decisions on the best treatment courses based on a patient’s unique profile.
Healthcare organizations should continue their scientific approach to data-driven hypotheses by being objectively regimented.
A benefit of embracing agility within data analytics is that the work lends itself to frequent objective scrutiny, thus doubling down on the ability to quickly pivot as needed during each feedback cycle.
Payer and provider organizations, however, should be careful not to get bogged down in analysis and set parameters that fulfill a “minimum viable product” description – the version of a product that allows data gathering and insight with the least effort. Refinement can come later in further iterations.
For example, an East Coast payer organization focused on the MVP of an enrollment and billing system implementation. This meant that the agile team was able to deliver value more immediately while having a thorough pipeline for future incremental enhancements.
Setting parameters for what makes an idea worth pursuing using a standard and repeatable process is challenging. But healthcare organizations should not shy away from trying this approach because it can yield many benefits, including shortening the time it takes to bring new solutions to the market.
To review, the three key steps to launching a successful agile strategy are:
- Set a clear direction that focuses on gaining incremental insights from a vast and growing repository.
- Create the conditions for success from the outset by encouraging curiosity that embraces failure, by focusing on data-driven hypotheses and by focusing on cross-functional teams.
- Prioritize, limit work in progress and iterate upon the discovery of data insights to drive strategic decisions.
The potential benefits of being able to tap into data to create fresh insights and deliver potentially life-changing results are vast and far outweigh the challenges involved. More organizations should leverage data analytics to take a leap forward in how healthcare is delivered.
Govert Klaassen is a digital expert at PA Consulting. Chad Wagoner is a healthcare and delivery expert at the firm, and Mike Haynes is an agile transformation and delivery expert.