Healthcare delivery in the future will look a lot different than today – and one major reason behind that is the COVID-19 pandemic. Although the pandemic has claimed more than 890,000 lives around the globe, it has also demonstrated how virtual technology and data integrations are powerful allies of healthcare in these modern times. The pandemic has proved to be a major boost to healthcare’s journey towards consumerism and eventually, to value-based care.
On the surface, these factors may appear as driving forces in healthcare transformation. Looking closely, we find that nearly all these advancements are dependent upon new technology and healthcare’s ability to harness the massive amount of data being produced. At the end of the day, healthcare’s transformation depends largely on how data management can be leveraged to produce real-time insights and support critical decision-making. However, there are some critical problems standing in the way.
Are healthcare organizations leveraging 100% of the potential of their data?
Short answer: not really. The first challenge is data fragmentation. The U.S. healthcare system uses many different kinds of data and multiple applications and more often than not, this data could be structured, unstructured, or semi-structured. To bring the data that was most relevant to patient health, healthcare organizations invested in systems of records such as electronic health records (EHRs), practice management systems (PMS) and billing systems. Deriving insights from healthcare data requires it to be unified and given that these systems are mostly siloed and have no way of communicating with each other, finding relevant information is always tricky.
Second is the volume of data being generated. According to a report, healthcare organizations witnessed data growth of a whopping 878% since 2016, managing 8.41 petabytes of data on average in 2018. Additionally, the global COVID-19 pandemic has added an enormous amount of data related to the confirmed cases, treatments, resource inventory, hospital beds and more. Healthcare has at its disposal, detailed insights into patient conditions, past medical history, drug-related information and social determinants of health. Along with the volume, maintaining the security of such information when leveraging it for decision making is also a significant challenge.
Why is data management so critical to your organization’s success?
In the past decade and a half, healthcare organizations have experimented with ways to bring healthcare data together. Some used EHRs to store data and leverage it to create digital patient records. Some organizations implemented an on-premise data warehouse to manage their data and get crucial information out of it. However, the data that was captured was not adequate to map out the full journey of care for a patient. Even if health systems and hospitals did try to pool in all the data together, they would be faced with as many as 18 different EHR systems to integrate and coordinate.
The problem is not just about volumes of data or even interoperability between systems for that matter. Sorting through large amounts of information and finding the important nuggets is something that our healthcare system is just not good at. The challenge is identifying which data is important and how can it be used to improve the performance of a health system or treat patients better.
With proper healthcare data management, organizations can achieve the critical goal of delivering truly personalized, patient-centered care. The move to value-based care has patient-centered care the top priority. But even with the right tools in place, coordinating care in an ecosystem as diverse and expansive as healthcare takes more than simply unifying data.
Besides collecting data and integrating it to provide patient-centered care, another dimension is to use this data and change it into applied intelligence, especially by using machine learning and artificial intelligence (AI). AI algorithms can become more precise and accurate as they interact with training data, allowing providers to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes. These algorithms along with digital assistants and clinical decision support can improve efficiency in processes and increase accuracy in care. However, while AI can assess what sort of problem is plaguing a population, what their healthcare needs are and what can be done to deliver better care, it can’t provide a solution. The solution and its execution needs to come from the healthcare professional– which is why AI has to be wielded by providers and needs to be backed by data. Leveraging AI must encompass some way to intervene and it’s impossible to plan this without well-managed data.
Additionally, improved and more efficient data collection and real-time data retrieval can help healthcare organizations reduce patient costs. If data can be more readily and accurately converted to useful information, then areas for improvement for a patient can be identified and serious complications can potentially be avoided. Faster diagnoses can lead to low costs while hospitals can optimize their resources in treating patients. Better data can inform preventive care and help providers identify people at high risk for certain conditions so they can work with them proactively to manage their health and avoid complications as much as possible.
Moreover, better data means better business decisions. The best healthcare organizations balance their need for profit margins with resource output for patient care. The better a facility’s healthcare data management is implemented, the better their analytics and business intelligence becomes. This often leads to identifying glaring issues as well as growth opportunities that just need a bit of quantification to justify a corrective move. For instance, if mammographies in a certain geographic area are down, noting trends in the data can point to potential underlying reasons that can be addressed. Once the problem is found, it can be attached to an appropriate metric and considered quantitatively.
We’re sitting on a goldmine of data!
Over the years, healthcare has accumulated a lot of data, and a large variety of it. However, managing all this data and using it to support decision making is a key part of a healthcare data management strategy. We can keep collecting data, but it won’t do much good if we’re unable to use it in a meaningful way to deliver on the value proposition we promise. Clinical records, billing information, social determinants of health, wearables – there are more than a hundred types of data sources available. The amount of data will continue to grow as healthcare incorporates data-intensive, next-generation diagnostic tools. Unused data is healthcare’s Achilles’ heel.
The right data approach will ensure a real-time and updated platform to support multiple operations for an organization – scheduling, patient referrals, care management, utilization management and more.
Healthcare is still catching up with harnessing big data in a practice way. Now, the goal is to transform data into meaningful information to support a value-oriented system. The value-oriented system requires an even more data-centric approach that focuses on the overall quality of care and the patient experience. This is a big shift, one that requires an expanded focus on data in the healthcare industry. It’s no longer just about treating the acutely ill. Health systems must now deploy significant data strategies that are capable of bearing the weight of population health, even in the face of a pandemic as devastating as COVID-19.