The healthcare economy is changing rapidly – from increased consolidation to the rise of consumerism in care, healthcare organizations face a market that requires a holistic understanding of their enterprise (from clinical to claims data) to succeed. To stay competitive and deliver the best quality of care and value, providers should think like payers and payers like providers. As such, strengthening integrated care remains a hurdle for organizations to overcome as they seek to improve clinical quality, reduce operational costs and support care management. To achieve these goals, healthcare systems must be able to have access to data not just within their own organization, but from outside sources – which is often siloed.
A HFMA Health Care 2020 report on consolidation points out that to succeed in an increasingly competitive marketplace, healthcare organizations are investing in data analytics capabilities to help them understand their patient – and entire business – better. While investing in analytics is an integral key to success, an overall best-practice strategy must be developed to make data actionable. Here are five best practices that should be adopted to initiate an analytics strategy:
- Identify enterprise champions – To ensure buy-in from key internal stakeholders, leadership and process changes must occur. Change to the entire organization’s attitude on data governance must come from the top and trickle down to the bottom.
- Find value in existing data – As new payment models are adopted, healthcare providers need to design a technical infrastructure that can integrate payer, health system and medical group data within an enterprise healthcare delivery system to create value. Organizations should leverage their core data set and claims data, but also pull in existing ancillary data to have a better understanding of their organization.
- Create data-driven culture – Establishing an enterprise analytics department ensures that the entire business is standardizing and handling data consistently, but also encourages the new analytics department to champion a holistic approach towards data management. Champions should include representatives across all departments, from clinical to claims teams.
- Outline and developing manageable goals – Instead of tackling all problems at once, start small. Is the organization focusing on obtaining a streamline, single view of their entire business? By setting a goal with real, manageable next steps, all stakeholders can quickly perceive value in an enterprise initiative.
- Train, train, train – Repeated trainings and regular communications across the enterprise ensure long-term initiative success. By holding teams accountable, while empowering them with resources to succeed, data sharing efforts across the enterprise are bound to improve.
Changing goals and evolving organizational structures require players in the healthcare industry to pivot quickly. Whether it’s to meet increasing consumer demands or to better align on value-based initiatives, organizations will need to rely even more on data to achieve their goals. When organizations embrace analytics, and have a go-to data analytics strategy, the procurement and actionable next steps will come naturally.
To learn more on how to take action with your data, check out our latest whitepaper here. If you are interested in ways we can help you on your analytics journey, learn more about our enterprise analytics options.
Get our take on industry trends
How to help employer groups plan in a time of uncertainty
Employers and their sponsored health plans are thinking about next year’s benefit designs with a significant challenge not seen before: the effect of the coronavirus pandemic. There are important considerations to take into account before making any decisions about new or existing coverage. Becky Niehus, a director of Product Consulting at MedeAnalytics, explores these new issues and what employers can do to ensure employees are “covered.”
Read on...Healthcare’s return to “normal” after COVID-19: Is it possible?
As providers determine how to get patients to return to facilities for routine disease management and preventive screenings, opportunities are ripe for the application of analytics to triage at the right time to the right setting. Data related to COVID-19 will continue to flow rapidly, but there are possibly more questions than answers now about a return to “normal.”
Read on...Avoid COVID-19 modeling pitfalls by eliminating bias, using good data
COVID-19 models are being used every day to predict the course and short- and long-term impacts of the pandemic. And we’ll be using these COVID-19 models for months to come.
Read on...Population Health Amid the Coronavirus Outbreak
In speaking with many colleagues throughout the provider and payer healthcare community, I’ve found an overwhelming sense of helplessness to the outbreak’s onslaught. This is exacerbated by the constant evolution of reported underlying medical conditions that indicate a higher risk of hospitalization or mortality for a coronavirus patient.
Read on...