June 20, 2022

How can data analytics be leveraged as a health inequity solution?

The ongoing COVID-19 pandemic has laid bare issues of health inequity not only across Germany, but also on a global scale. Medical deserts and pockets of limited or non-existent provision of both specialised and basic healthcare are becoming more apparent, with individuals in certain regions unable to access appropriate care.

How can data analytics be leveraged as a health inequity solution?

The implementation of data analytics solutions is gaining traction as a potential answer to a multitude of issues when it comes to ensuring equitable and fair access to quality treatment within the healthcare sector. COVID-19 has put substantial pressure on existing healthcare systems and providers, exposing a large number of disparities based on a variety of factors, including but not limited to socio-economic status, location, age, and racial and gender identity. Many individuals both within and outside of Germany find themselves in a position in which they are unable to access quality care within a reasonable distance or time period, resulting in gaps in treatment.

Although the vast majority of medical deserts can be found in rural areas, a growing number of urban and suburban regions have found themselves experiencing a disproportionate level of access to care. Whilst medical deserts are technically defined as a region in which residents live at least a 60 minute drive from a hospital with trauma care services, the term can also be applied to areas lacking basic non-emergency care structures, such as dental services, for example. 

Long-term limited access to appropriate healthcare is harmful to both individuals and wider society, creating a ripple effect of wider health and social issues, such as reduced life expectancy and higher rates of avoidable problems, such as obesity and substance abuse issues. This disparity results in poorer patient outcomes and higher regional rates of certain diseases and conditions, causing undue pressure on existing medical facilities and services. New measures ought to be taken in order to improve the average health of a population and reduce marked health disparities, with the implementation of data analytics solutions standing as a potential fix to this growing issue.

Public health measures which reduce issues of health inequity are a cost-effective and sustainable solution to the increasingly prominent crisis of medical deserts. Data analytics has contributed significantly to the improvement of patient outcomes, breaking down some of the barriers that exist in today’s healthcare system. The adoption of telehealth services can be seen as a good starting point – but there are a number of other innovative ways in which data analytics is set to benefit the healthcare sector.

Throughout the ongoing COVID-19 pandemic, data analytics solutions have enabled healthcare providers to prepare effective population health management responses based on insights derived from the analysis of data across a variety of different spectrums. Healthcare providers have been presented with a unique opportunity to identify areas for development and growth through the widespread employment of data analysis, and high-quality analytics are now considered to be essential for organisations wanting to create a 360-degree picture of their clients, customers, and patients.

Leveraging data analytics solutions alongside broad data collection measures offers a unique insight into real-world experiences within the healthcare sector, highlighting problem areas that require attention. The analysis of historic data and trends allows healthcare providers to recognise patterns and anomalies within certain fields, helping pre-emptively stop the problem at the root before it develops into something that requires more attention.

The implementation of artificial intelligence provides healthcare providers with an opportunity to offer better care to patients located within at-risk regions, easing their workload and increasing the likelihood of successful outcomes based on predictive and prescriptive analytics. Although there are obvious benefits for individuals, the widespread collection of data creates a more diversified data bank, resulting in more accurate and reliable insights that can help physicians guide treatment options more effectively.

Collecting large volumes of data comes with its own challenges, both from a practical and ethical perspective. Fortunately, AI techniques – such as deep and machine learning – enable analysts to quickly comb through enormous amounts of data in a reasonable amount of time, resulting in the production of valuable insights that better represents diverse populations, leading to authentic and precise recommendations and suggestions that accurately reflect the community in question.

Addressing issues of poor access to treatment and reducing health inequity results in a better quality of life for affected individuals, easing pressure on healthcare systems and improving existing services. A solutions-oriented approach to medical equity is entirely possible, with patient-reported data playing a major role in its development. 

Highlighting the invaluable information derived from data analytics and applying these actionable insights at a population level will enable everyone from data analysts and medical researchers to healthcare providers to make real progress in closing the equity gap across the patient care spectrum. When used responsibly and ethically, data analytics has the potential to make big changes in the healthcare sector by targeting inequities and reducing disparities, and we shouldn’t’ underestimate it. There are many significant and substantial challenges when it comes to the practicalities of improving healthcare multi-dimensionality, and only by overcoming these will the healthcare industry be able to fully realise the benefits and advantages this innovative approach can offer us.