By Inga Shugalo
With the gradual transition to prevention, population health management (PHM) is gaining momentum, and so is associated software. According to the report by MarketsAndMarkets, it’s a booming market that will steadily grow at a CAGR of 11.5% till 2025. And yet, PHM adoption is ridden with serious challenges. Surprisingly, clinicians often cite technical complexity among the top five reasons blocking them from using PHM software. So is it really that difficult?
Technologies at hand
Now let’s look at population health management software. It has three key technical elements — data sources, big data analytics, and care journey automation. The latter is not a big problem, as many PHM software vendors provide ready-made pathways that can be customized with no need to develop them from scratch. But what about the other two?
Let’s start with data sources. Population health management software aggregates data from clinical solutions, and you may already have the needed sources right at your fingertips. Because of the pandemic, healthcare facilities had to embrace remote technologies: telemedicine solutions, patient monitoring devices, and more, in order to provide healthcare services safely and reduce the number of visits to clinics for low-risk patients. These solutions can supply the necessary data.
The pandemic has also positively impacted the RMP devices market: in their RPM Systems report, ResearchAndMarkets states that the market will double by 2027. One remote healthcare technology may spur another like the domino effect.
Still, you should remember that you don’t have to deploy every single element of the modern digital ecosystem, which may feature components such as blockchain and artificial intelligence. Just the two touchpoints above — telehealth and remote patient monitoring — may be enough to get started with population health management. You may introduce other solutions later whenever you see fit.
So we’ve figured out that telehealth and RPM devices are pretty much sufficient for launching PHM. However, those are just source data generators; later on, the data needs to be transmitted to EHRs and processed for analytics. For this matter, you need high-quality, clean data. But is the EHR data of high quality by default? Unfortunately, it’s not. EHR data is not immune to such downsides as incompleteness, occasional inaccuracy, and bias.
To make sure your EHR data is suitable for analysis, you may use data audit tools and frameworks. Obviously, manual verification here is not an option, as it is too time- and effort-intensive.
A treasure chest of analytics
PHM solutions offer rich analytics capabilities, a real treasure chest. First of all, there’s big data analytics. Big data tools examine medical histories and divide patients into cohorts, relying on the risks of relapse or other negative events. What’s more, patient health analytics can be used for a larger territory — a city, a region, and even the entire country. In 2019, researchers used the technologies mentioned above on 60 million patient records from 50 US states to predict the likelihood of type 2 diabetes development in pre-diabetic patients.
Population health patterns develop under five determinants — genetic, social, medical, environmental, and behavioral. And here comes a surprising discrepancy — while social determinants have a big impact on health outcomes, they are nowhere to be found in clinical datasets. To aggregate social determinants of health (SDOH), clinicians need either to run person-to-person interviews or to monitor their patients to detect tell-tale signs of hazardous social factors. However, this is time-consuming and prone to errors.
Luckily, it’s possible to cover social factors with the help of big data analytics, too. These tools can extract raw data from multiple non-clinical sources, normalize it, and aggregate within patient profiles.
As we know, clinicians have little time to dig through data, trying to build strategies. With PHM tools, big data analytics does that for them. To save time and efforts on studying the delivered data, you can transform it into easy-to-understand, user-friendly dashboards with data visualization tools. This will support your fast and informed decision making.
Population health management solutions are powerful and yet vulnerable: their analytical capabilities and automated clinical workflows don’t work if patients don’t adopt data-generating tools. And here comes another challenge — patient engagement.
With the recent shift from reactive to proactive care, patients are no longer passive. They are partners who work together with providers to ensure the best possible outcomes and reduce the rate of readmissions and related cost.
So how to stimulate patient adoption? Just introduce them to a solution they would really like to use for controlling their health better at any time. For this matter, mobile healthcare apps make a suitable choice. There are about 276 million smartphone users in the US today, which makes about 84% of the US population. So your app is likely to find its users.
What’s more, to improve the chances of swift app adoption, you may start with some chronic condition management solution, as chronic-condition patients are typically more open to proactive health management.
It’s worth noting, though, that healthcare facilities are not the only ones providing condition management apps, and that those made without professional guidance and expertise may actually harm users. What’s worse, such unprofessional apps don’t activate alerts when, say, the blood glucose level is too low or high. It may put app users in life-threatening situations. So deploying a professionally reviewed app for chronic-condition patients, you may save many troubles for them and for yourself.
There’s another element to it — a marketing one. If enjoying a safe and useful app, chronic-condition patients may spread the word among other patients to foster their interest in having one for them, too. These apps can be simpler, with such key features as the safe access to the EHR for health management as well as appointment scheduling.
As we can see, getting started with PHM is not as complex as it might seem. You don’t need to dive into deploying each and every digital solution separately — you may already have the foundation in the form of telehealth and RPM solutions. The two areas you do have to look into are EHR data quality management and patient engagement.
A mobile tool for chronic-condition patients might be the most suitable solution to propel engagement. However, to make sure such an app is useful and offers help in dire situations, you have to work on precise requirements to be covered and reviewed during development. In this case, due efforts pay off — you can get many engaged patients while also launching full-scale PHM, contributing to enhanced care quality and reducing costs.
Inga Shugalo is a Healthcare Industry Analyst at Itransition, a custom software development company headquartered in Denver, Colorado. She focuses on Healthcare IT, highlighting the industry challenges and technology solutions that tackle them. Inga’s articles explore diagnostic potential of healthcare IoT, opportunities of precision medicine, robotics and VR in healthcare and more.