When we asked healthcare leaders if they trust their data’s accuracy and reliability, most (80%) said yes. But, only 53% of this data informs business decisions. So how can healthcare leaders put more of their data to work for business and clinical decision-making?
Data platforms can unlock data’s potential, enabling better patient care and decision-making, but adopting a fully centralized data platform can be challenging. Mike Tiffany, Chief Operating Office at Arcadia, and Shashi Vangala, SVP, Chief Data Analytics Officer at Ochsner Health recently discussed barriers to data activation and ways to release its potential. Watch the full webinar.
Across the industry, healthcare leaders want to activate data to enhance quality of care. However, there are a few differences in which priorities to tackle first based on organization size and structure. Organizations with revenue under $1 billion prioritize improving workforce productivity and using data for compliance and auditing. In comparison, organizations with revenue over $1 billion prioritize research and innovation at higher rates.
Single-facility healthcare organizations are less likely than multi-site ones to use data for preventative care and population health management. But, single-facility organizations have a slightly higher interest in prioritizing data for workforce productivity.
Healthcare organizations may struggle to improve care quality and meet important goals if they don't leverage their data. This can cause four big challenges: too much data, outdated data, operational issues, and disagreements among leaders. Let’s review each hurdle and the resources to resolve them.
While it can appear from the percentages that healthcare organizations are using less data to make decisions, in fact, the volume of data has grown as technology has given them the ability to mine data from more unstructured sources. This means that they may actually use more data, but it’s harder to utilize a high percentage of the data available. Instead, it is important for these organizations to strike the right balance between accuracy, reliability, completeness, and timeliness when there's so much data to sift through.
While point of care decision-making and care management tasks benefit the most from real-time data refreshes, less than half of leaders report that this is going on. Conversely, more than half say IT systems refresh in real-time. So, does all data require real-time updates? The answer is that it depends on the use case, which will highlight the need for trade-offs between recency and completeness. For instance, patient care applications require data to be very timely, but for primary care doctors seeing chronically ill patients, trying to understand what happened in between visits, completeness is extremely important. Ultimately, the coordination of data sources and the platforms of work comes down to cost benefit analysis.
Research shows that data utilization can alleviate burdens associated with workforce shortages, care team burnout, and higher operating expenses, but there's a knowledge gap when it comes to data literacy and effective collaboration among teams. Automating key workflows to leverage in demand insights can save hundreds of hours clinicians spend searching through data to find key information.
Healthcare leaders agree on data’s role in their success and think data platforms are key to creating a reliable data asset. Where they don't agree is on the purpose of a data analytics platform. Some want a platform to support patient care; others want one to support strategic planning and decision making. These disagreements should be expected, embraced, and drive the alignment of the users of the system that is ultimately put in place along with strong governance to get the right data in the hands of those who need it.
Unlocking data’s potential requires good change management in both the short and long term. Initially, roadblocks stem from platform adoption, but over time handling the ongoing change and maximizing the platform benefits will be crucial for success.
There are a few best practices for encouraging adoption. First, enter the project with clarity on scope and timelines. Second, identify the challenges that moving to a new platform creates and find ways to include your users during the process so that they feel like they're part of it. Third, roll it out intentionally, and think about when to roll out to each user community. Fourth, have an internal champion and communicate the platform’s value. And fifth, have an open discussion with vendor partners.
For smaller organizations and those that haven’t adopted the IDN model it's a missed opportunity. IDNs, CINs, ACOs understand the importance of data to care delivery and financial success. A platform unlocks better performance across a network and at the point of care — it enables the right care delivery at the right time, helps identify and close gaps, and gives the entire organization a better view of patients and populations.
Over half of healthcare leaders have integrated core technologies with a data analytics platform — care management, quality measurement, patient engagement tools, self-service reporting, and aggregation of structured data — but are they ensuring this data is interoperable? Interoperability is a full-circle data transfer and activation. To better activate data, organizations must aggregate it from various sources and then repackage and deliver it to end users in a way that’s useful to them without having to worry about the format of that data, all with a strong governance structure in place.
At the time of this survey, AI, non-traditional data like SDoH, and unstructured data were the most underutilized applications integrated with a data platform. These areas that are ripe for transformation and opportunities for organizations to take the lead and disrupt healthcare.
Non-traditional data such as SDoH, census data, and SOGI and unstructured data such as NLP, notes, and audio can help complete the picture of the whole patient.
The easiest first steps for AI are on the business efficiency side of the business rather than clinical. Consider use cases: revenue cycle automation, prior authorization, triage/patient communications, call center work, and knowledge retrieval.
Contracting and risk suspecting are almost equally split with plans to implement and already implemented. Very few don’t plan to implement it. As models get more complicated and organizations enter more relationships with various payers, this is going to be an important area to get right.
At the end of the day, data helps drive three key areas: care delivery, workforce productivity, and innovation. Healthcare organizations grappling with policy changes like CMS’ 2030 initiative, delivering on consumer expectations, managing budget constraints, and competing in a consolidating marketplace can find strength in data utilization.
Watch the full webinar to learn more. And request a one-on-one demo of Arcadia’s data analytics solutions to see what Arcadia can do for you.