The purpose of healthcare boils down to the well-being of every single human being. The collective responsibility to ensure physical, physiological, and emotional well-being is on every stakeholder in the healthcare spectrum – starting from policymakers to the last lines of implementers.
When we address the existentialist question of healthcare’s purpose, a critical aspect emerges – population health management. In this article, we will exclusively discuss what population health management means and how it can be optimized using emerging technologies, specifically NLP models.
Let’s get started.
What Is Population Management?
In simple words, this is the process of studying, observing, and facilitating healthcare for a specific group of individuals to improve their lifestyle and overall well-being. For instance, population management ensures practices and protocols to improve the standard of life and health for patients suffering from cholera, yellow fever, dengue, and other ailments.
At this point, it is equally important to understand the difference between public health and population health management so we have a systematic approach to achieving our vision.
Public Health
Public health and population management are two often misinterpreted terms. Public health is about:
- Formulating strategies and laws such as safety standards, hygiene benchmarks, and more
- The tracking and monitoring of disease outbreaks
- Implementing corrective measures to mitigate outbreaks
Population Health Management
On the other hand, this aspect is all about:
- Assessing the risks and scope of improvements for the population suffering from specific diseases such as Alzheimer’s
- Data analytics to improve their conditions
- Deploying strategies to eradicate them in the future and more
To be more specific, Covid-19 protocols such as shelter-in-place are public health initiatives while treatment of Covid for patients suffering from pre-existing conditions such as diabetes or heart disease is part of population health management.
To facilitate population health management, we need to process incredible volumes of patient data to detect patterns and manually analyzing vast amounts of patient data from Electronic Health Records (EHRs) is time-consuming and prone to human error.
However, the good news with us is that we can leverage emerging technologies such as AI and its myriad concepts like NLP to implement population management strategies.
The Role Of NLP In Bulk Data Processing For Population Health Management
NLP empowers healthcare providers and public health officials to extract valuable insights from EHRs, streamlining population health management efforts. Here’s how:
Disease Prevalence Mapping
- NLP models have the ability to analyze diagnostic reports from across a large population to identify areas with a high prevalence of specific diseases. This enables targeted interventions and resource allocation to address these areas.
- For example, identifying a cluster of diabetes cases in a specific geography/region through NLP analysis can propel targeted public health campaigns promoting healthy lifestyle choices and preventive care.
Risk Factor Identification
- NLP can analyze critical data points such as patient demographics, medical history, social determinants of health, and other information to identify individuals who are at a high risk for developing certain diseases.
- Such an early identification of risk factors enables healthcare professionals to intervene with medical procedures at just the right time, reducing the burden of chronic diseases.
- NLP tools can analyze patient narratives and medication adherence data to identify individuals at risk for poor medication adherence, allowing targeted interventions to improve treatment outcomes.
Tailoring Public Health Interventions
- Analyzing patient data with NLP paves the way for customized public health interventions based on specific population needs.
- Additionally, NLP can effortlessly analyze patient feedback and even compile data from diverse social media platforms to understand public perceptions and concerns about specific health issues, allowing for more effective communication and outreach strategies.
Advantages Of Using NLP For Population Health Management
With NLP, possibilities are advantages are aplenty in optimizing population health management. Let’s look at the most predominant benefit:
NLP automates data analysis and reduces reliance on manual processes, saving valuable time and resources.
NLP algorithms can analyze vast amounts of data with greater accuracy and consistency compared to manual analysis.
NLP can extract specific details and patterns from patient data, providing deeper and more nuanced insights into population health trends.
NLP allows for identifying individual risk factors and tailoring interventions to individuals' specific needs.
NLP facilitates evidence-based decision-making by providing reliable data on population health trends and needs.
Challenges In The Process
While there are incredible advantages to the integration of NLP in processing bulk data for population management, there are also specific challenges that prevent stakeholders from leveraging the technology to its fullest. These include concerns in:
- Data quality and accessibility: where standardizing, integrating and harmonizing diverse data sources is a bottleneck
- Algorithmic bias and fairness: Depending on the quality of data sourcing and compilation, models can be biased in terms of outcomes and responses
- Ethical considerations: such as data privacy, transparency, and trust in NLP models and more
The Way Forward
The implementation of NLP can revolutionize the way population health is managed and analyzed. By uncovering and offering valuable insights from EHR data, NLP is empowering healthcare stakeholders such as professionals and public health officials to improve population health outcomes, reduce healthcare costs, and foster preventative care. As NLP technology continues to evolve, its potential for improving public health will continue to grow.
If you have an enterprise vision to implement NLP for healthcare, get in touch with us to source high-quality ethical healthcare data. This could be to train your AI models or assist in clinical research and breakthroughs. Our expertise in this domain will complement your business visions and goals. Reach out to us today.
