Healthcare providers collect, manage and store large amounts of data daily, but whether that data is used to optimize care delivery is still a work in progress.
Take the collection of social determinants of health data, for example: Recent research has found that while many healthcare organizations are collecting SDOH data, challenges remain in integrating such data into an electronic health record and taking needed action on it.
As payment models and financial pressures push health systems to evolve, having better data and analytics will only support critical decision-making and help organizations on their digital transformation journeys to meet changing patient expectations and ease clinician burnout.
The strategies to access and integrate healthcare data — from both inside and outside hospital walls — are changing, including the need to leverage more cloud capabilities and rely on modern data platforms. But this change doesn’t have to happen in isolation.
Healthcare-Specific Maturity Models Can Offer Key Guidance
Maturity models are helpful for setting expectations and driving communication between IT staff and leadership inside healthcare organizations by defining current states and desired future states of capabilities and maturity. Depending on what your organization is looking for, a simple, more informal model may be enough to communicate your strategic vision.
The HIMSS Adoption Model for Analytics Maturity is one industry roadmap that can guide healthcare organizations. There are several others, and it’s up to healthcare organizations to decide whether they’re looking for external-facing recognition for having a certain level of maturity or if they just need internal guidance.
Maturity models are helpful, but organizations need to understand why they’re using which model, factor in any associated costs and clearly define their goals. Healthcare organizations that are just starting on their journey can turn to peers that have advanced in this area, because collaboration is integral to data maturity. There's value in seeking partners to help at every stage of maturity, from the basics and building out data science teams to specific advanced analytics and strategies for artificial intelligence and machine learning capabilities.
Click the banner below to learn how a modern data analytics program can optimize care.
How Can a Health System Start on Its Data Maturity Journey?
The first step is to clearly define who the champion is for data and analytics within the organization and have a clear understanding that the organization’s leaders are committed to making better data-related decisions. It’s difficult for a new partner when there's no clear support from the top for using data to actually improve organizational and clinical operations. That kind of commitment will have a bigger impact in the long run on the effectiveness of the data.
Next, the organization should document what the drivers are and map what it can't achieve effectively without the support of data and analytics. This should be in writing as a core competency.
READ MORE: How modernizing data storage leads to better data access in healthcare.
Finally, the organization should reach out to a partner to align on goals. It isn't just about shiny, new technology, it’s about getting results and outcomes from data – an acknowledgement that the health system cares about people and processes just as much as technology. By having a better idea of who its leaders are and what its commitments are, the organization will be better equipped to find the right partner to help it along its data analytics journey.
A mature data program touches every facet of healthcare operations if it's done right. It takes the discipline of leaders to actually use data in a meaningful way to affect change. Two areas of focus to start would be data platform modernization and data governance.
It's important now to take the next steps and learn from others so that your organization can take a holistic approach to its data program. It takes work, but it’s worth the effort. Don't treat it as just another IT project. It is very much an organizational commitment that must be made to use data as a strategic asset.
This article is part of HealthTech’s MonITor blog series.
kitsana pankhuanoi, sturti, Hiraman, peterspiro, FG Trade/Getty Images
