medical data analytics
Despite their increased popularity, most traditional wellness programs often fall short on delivering their promise of healthier, more productive workforces as advertised. According to a new Harvard University study, that’s because most wellness programs don’t really address the needs of the most vulnerable employees. If that makes you wish you had a crystal ball that helps you see into the future before investing your dollars, we have good news for you! From diagnostics to management, Population Health Informatics is just what you need to effectively target the root cause of employee health issues.
In this article, we’ll explore the data-driven world of Population Health Informatics and how it empowers business executives to make informed, strategic decisions about employee health and well-being. We will also provide concrete data and real-world examples to show how population health informatics can reduce healthcare costs, improve employee health outcomes, and increase productivity.
Click on the sections below to read on about a specific topic:
Population Health Informatics is the “application of technology to improve population health outcomes.” In simplest terms, it is the meeting point between data, healthcare, and technology, allowing health experts and data wizards to join forces and identify hidden patterns, trends, and insights. This helps them create a more accurate and comprehensive view of the health of the workforce and communities, enabling organizations to make better-informed decisions that improve employee well-being.
For business executives, Population Health Informatics is as indispensable as a GPS system, guiding you through the unfamiliar and often unpredictable world of employee population health improvement. It is the guiding light you need in your health and wellness journey, providing valuable insights and directions for the most effective health interventions. As in other executive decisions, you can’t afford to overlook data and trends in your population health management efforts. Here’s why:
While these figures point more toward population health management at the national level, the underlying principle remains. Harnessing the power of data and technology can enable all stakeholders to identify health risks before they become full-blown problems, allowing them to prioritize easily accessible, low-cost solutions for the high-risk, high-cost sub-population first.
So, how does a population health informatics system work? To understand that, let’s look at the key components of population health informatics.
As we discussed earlier, Population Health Informatics is all about health-related data. It is about connecting the dots between data points to understand the root causes of health outcomes or disparities. This requires data collection, analysis, and interpretation before designing suitable interventions.
The first step in population health informatics is gathering health-related data from various technology applications. These include electronic health records, insurance claims, telehealth and wearable devices, and social media. This comprehensive data collection helps paint a detailed picture of the health status, patterns of social determinants of health, and even individual behaviors of employees.
The second step in the process is integrating the information collected from multiple sources to create a unified, holistic view of your employees’ health. This involves relating different datasets to help you understand your workforce’s health landscape.
With integrated data in hand, population health informatics employs advanced analytics techniques to identify trends, patterns, and disparities within your employee population. This will then help uncover hidden health risks, reveal opportunities for targeted interventions, and provide the basis for informed decision-making.
Population health management is a collaborative undertaking, requiring every stakeholder to pull their weight. Sharing data insights is crucial to that collaboration, allowing concerned parties such as healthcare providers, HR, and even public health agencies to collaborate well in developing and implementing data-driven strategies to improve employee health and well-being.
The data insights will ultimately guide where to invest the organization’s resources. For example, what are the barriers to each segment’s well-being? Do most employees require support accessing a primary care doctor for routine appointments or wellness incentives? By tailoring these interventions to the specific needs of your workforce, you can ensure maximum impact, improved health outcomes, and more efficient use of resources.
Lastly, Population Health Informatics involves continuously monitoring and evaluating the implemented health interventions. This feedback loop allows you to measure the effectiveness of your health initiatives, refine your strategies, and make data-driven decisions to optimize your approach to employee health and wellbeing.
Earlier on, we briefly touched on how population health is always on a continuum at any given time. The pyramid below does a good job of depicting the healthcare needs of a population and the corresponding interventions per category.
As you can see, each category demands a distinctively different management approach with unique health outcomes. For example, while the healthy majority requires infrequent checkups occasionally, the 1% with a high disease burden will often require specialized attention. From primary care physician visits to specialist consultations to MRIs to filling prescriptions, the healthcare costs for this group increase quickly. The aim of population health informatics is three-pronged:
Population health management champions the prevention of costly illnesses and keeping the population in perfect health for as long as possible. However, that can only happen if stakeholders can see the trends and identify individuals at risk for various illnesses. Tools like machine learning algorithms and advanced analytics can further enhance the accuracy of predictions and the effectiveness of interventions.
American healthcare today is shifting from case management to care management. Unlike the traditional disease-centric model, the population health framework focuses on streamlining care management and building timely and accurate information, allowing healthcare providers to gain a comprehensive view of a patient’s health.
Furthermore, informatics tools can help identify gaps in care, monitor patient adherence to treatment plans, and facilitate communication between healthcare providers, patients, and their families. This results in more efficient and effective care management and better health outcomes.
Timely clinical care accounts for less than 20% of the population’s health. On the other hand, social determinants of health (SDOHs) like socioeconomic status, education, income, physical environment, and social support account for over 60% of health outcomes and disparities. Population health informatics helps identify these SDOHs, allowing the stakeholders to design targeted interventions and policies that address the needs of vulnerable individuals.
As a relatively new field, population health informatics encompasses various branches that focus on different aspects of analyzing and managing population health data. Some key branches include:
As we’ve seen thus far, population health informatics plays a crucial role in revolutionizing population health. That’s not just theoretical, either. Many companies have leveraged the power of data and technology to create tailored health interventions with a lasting, positive difference in the lives of their employees. Let’s look at a few real-world examples showcasing the tremendous impact Population Health Informatics can have on organizations:
In partnership with the YMCA and Walgreens, UnitedHealth Group launched an employer- and community-based Diabetes Prevention Program (DPP) based on a Centers for Disease Control and Prevention (CDC) curriculum in 2010.
The program utilized health data from participating employees to identify those at risk of developing type 2 diabetes and provide them with evidence-based solutions. The program was a resounding success, according to a 2013 study. Outcomes included:
Geisinger Health System, a healthcare provider in Pennsylvania, launched the Fresh Food Farmacy program to address food insecurity and diet-related chronic conditions among patients in selected zip codes in 2016. Using data analytics, the program identified patients with type 2 diabetes and provided them and their families access to healthy food, nutritional education, and support.
“Our initiative has had a greater impact on diabetes control than expensive medications that have significant side effects,” the team reported a year later. “We have also seen significant improvements in patients’ cholesterol, blood sugars, and triglycerides — improvements that can lower the chances of heart disease and other vascular complications.”
Population health informatics has also been used to identify and address health inequalities, ultimately improving health outcomes for vulnerable populations. One example of this use case is the Chicago Health Atlas, a free, web-based tool developed by the Chicago Department of Public Health and the Smart Chicago Collaborative.
This platform allows users to explore health data from various sources, identify health disparities in Chicago neighborhoods, and develop evidence-based interventions to address health inequalities in the city.
Omada Health offers a digital health program that uses data and technology to provide personalized coaching for patients with chronic conditions. The program combines data from connected devices (e.g., wireless scales, blood pressure monitors) with human coaching and support to help patients make lasting lifestyle changes.
Between 2019 and 2020, Omada studied a digital physical therapy (DPT) program for muscle and joint pain in 814 adults. By engaging patients in their health management and providing tailored support, the program showed remarkable improvement in clinical health outcomes, including pain reduction and function improvement. Better still, it reduced healthcare costs significantly, saving patients costly treatments like ultrasounds and electrical stimulation.
Kaiser Permanente Northern California’s Advance Alert Monitor (AAM) program is an excellent example of how Population Health Informatics can improve patient safety and quality of care. Launched in 2013, the AAM program is an early-warning system designed to predict and prevent life-threatening clinical deterioration in hospitalized patients.
The system utilizes machine learning algorithms to continuously analyze electronic health record (EHR) data, including vital signs, laboratory results, and other clinical information, in real time. This enables the care team to identify patients at risk of clinical deterioration within the next 12 hours, allowing them to intervene early.
According to a study published in the New England Journal of Medicine in 2021, the AAM program has significantly reduced in-hospital mortality rates, lowered the incidence of ICU admission, and shortened hospital stays.
Population Health Informatics offers several opportunities to improve healthcare at the population level, making it more effective, efficient, and personalized. Key opportunities include:
As the volume and diversity of health data sources grow, integrating this information has become even more critical. Organizations must now develop robust methods to combine data from electronic health records, wearables, and other sources to stay on top of employee health needs. This will allow for more comprehensive and accurate analyses, leading to better-targeted interventions and improved health outcomes.
As AI algorithms become more refined, identifying health trends, predicting disease outbreaks, and optimizing healthcare resource allocation will become even easier. These advancements will help inform decision-making and enable more effective population health management.
As our understanding of the complex interplay between social determinants of health, individual behaviors, and health outcomes grows, personalized medicine and precision health will become increasingly important. The future involves leveraging advanced analytics to develop individualized health strategies and targeted interventions. This will lead to more effective and personalized healthcare, ultimately improving population health outcomes.
Finally, we can’t overlook the importance of remote monitoring systems in population health management. Since COVID, this industry has exploded, making healthcare even more accessible. As the industry grows, Population Health Informatics will involve integrating telehealth data from emerging technologies with other health data sources, allowing for more accurate and comprehensive assessments of population health.
While population health informatics holds great promise to revolutionize healthcare, several challenges must be addressed for successful implementation.
Trust is the heartbeat of the healthcare system. With vast amounts of sensitive health data collected and analyzed from various sources, data privacy and security should be every organization’s top priority. Patients need reassurance that their information is safe from prying eyes, including their colleagues, bosses, and even family members.
Despite cyberattacks and data breaches being increasingly common, organizations must protect employees’ personal information from unauthorized access and misuse for everyone’s benefit. To avoid penalties, they must also comply with data protection regulations, like HIPAA. This requires constant vigilance and investment in robust security measures, such as encryption and secure control access.
To unlock the full potential of Population Health Informatics, healthcare providers and organizations need to share data with each other. However, this can be challenging due to privacy, competition, and data ownership concerns.
Another hindrance is the wide variety of data formats, systems, and protocols. Adopting common data standards and promoting interoperability is essential to overcoming this challenge. Organizations must also establish clear governance policies and agreements among stakeholders to facilitate data sharing and maintain trust.
Population Health Informatics requires a skilled workforce capable of managing, analyzing, and interpreting large volumes of data. Investing in workforce development, training, and education can help bridge any gap and ensure the successful implementation of informatics initiatives.
As Population Health Informatics deals with sensitive patient information, it must navigate a complex legal and ethical landscape. Healthcare organizations, insurance providers, and employers must stay up-to-date with relevant laws, regulations, and ethical guidelines to ensure that informatics initiatives are compliant.
Health data is complex and diverse, so you need a robust analytics system. Like in other industries, healthcare is adopting (and benefiting greatly from) machine learning and predictive analytics.
As they become more sophisticated, these platforms are becoming increasingly adept at processing and analyzing large volumes of diverse health data and identifying patterns and correlations. As a result, they’re allowing healthcare organizations and policymakers to make informed decisions and implement targeted interventions.
Population health is about collaboration. This makes robust health IT infrastructure and networks crucial for the secure and efficient exchange of health data. These networks facilitate the sharing of information between different healthcare systems, platforms, and stakeholders, which is crucial for understanding population health trends and improving care coordination.
As digitization takes over, we no longer need filing cabinets and dusty shelves to store health data. That’s because cloud-based services and applications offer scalable and cost-effective solutions for storing, managing, and analyzing population health data. More organizations are adopting these services, and the industry is, in fact, expected to grow to USD 25.54 billion by 2024.
Wading through vast piles of complex data creates room for errors and oversight. Luckily, data visualization tools have been developed in recent years, allowing for the effective representation of complex health data in a visually appealing and easily digestible format. This makes it easier for healthcare professionals and policymakers to understand and interpret population health trends, uncover hidden patterns, and make better-informed decisions.
Population Health Informatics is a vital field that offers many opportunities for forward-thinking business owners and HR practitioners. With cutting-edge tools and technologies such as machine learning, database management, cloud computing, and data visualization debuting daily, businesses have no excuse in revamping their population health initiatives.
As they continue to recognize the value of Population Health Informatics, it is essential for businesses to invest in the development and adoption of these tools and technologies within their organizations. This will ultimately drive a paradigm shift in employee wellness from a reactive, one-size-fits-all approach to a proactive, personalized, and data-driven model. In the end, this transformation will benefit the individual employees and contribute to a healthier, more productive, and more resilient organization as a whole.
Workplace financial wellness is more than a trending perk, it’s a core pillar of employee…
When it comes to encouraging physical activity in the workplace, it’s important to design employee…
As we approach Social Wellness Month in July, now is the perfect time to explore…
Employee wellness isn't just about offering gym stipends or access to meditation apps. It's a…
Employer branding has quickly become a critical focus for companies competing for top talent and…
Workplace leadership plays a defining role in the success of any wellness initiative. It’s not…
This website uses cookies.
Read More
