Why Data Accessibility in Healthcare Leads to Better Clinical Outcomes

The healthcare ecosystem creates, uses, shares, and stores enormous amounts of data. This data has become a critical component in healthcare, yielding many benefits.

From operational efficiency to better clinical outcomes, data enables improved analysis and informed decision-making. However, accessibility to and regulation of data remain key obstacles to unlocking its full potential.
Healthcare data is often stored in siloed systems, resulting in less-than-optimal sharing and interoperability. Likewise, a global patchwork of country-level requirements creates additional barriers to achieving global data accessibility.
Given these challenges, what steps can the healthcare industry take to enhance global data accessibility? Let’s take a look.

What Do We Mean by “Global Data Accessibility”?

One of the key concerns related to accessibility is that most data used at the population level is not representative of the global population. The data currently used in medical research tends to have a bias toward US and European populations,  overlooking billions of people. The goal of global data accessibility would be to make sources of  underrepresented real world data (RWD) more readily available for research purposes.
To better understand how we can address this gap, we must look at the current state of data access.

What Is The Current State of Data Access?

The last few years have accelerated healthcare data collection and usage significantly.
The pandemic changed the way many healthcare and life sciences organizations think about data sharing. Whereas previously, there was a reluctance to share data that might lead to competitive innovations, the threat represented by COVID to the world’s population spurred unprecedented collaboration in an effort to save lives. This willingness to share data may not survive the post-COVID era, but the advantages of doing so are quite clear.

Examples of Global RWD Data Sources

The following databases provide varying levels of access to global RWD:

  • WHO (World Health Organization) maintains a wide range of data collections related to global health and well-being. It includes data on diseases, policies, and social determinants of health (SDOH) from 198 countries.
  • The World Bank has global healthcare datasets on life expectancy, leading causes of death, Health, Nutrition, and Population (HNP) statistics, and service delivery for African countries. The organization’s datasets align with its missions to end poverty and promote sustainable, shared prosperity.
  • The Global Health Data Exchange (GHDx) includes measurements of the world’s most challenging health problems and how policies impact them. This data is free to access and particularly applicable to those developing policies.
  • The Demographic and Health Surveys (DHS) Program collects, analyzes, and shares data on population health representing 90 countries.
  • ArcGIS Open Data is available by subscription for medical researchers and serves as an aggregator for data from many open sources across the US, Europe, and Asia.
These are just a few examples of data catalogs that help facilitate the sharing of global data. While these and other sources of data exist, the raw data often lacks the clinical context necessary to drive improved outcomes for global populations. Improving access to these data sources, as well as RWD from an increased number of countries, will improve medical research and create many benefits for global healthcare.

5 Benefits of Improved Data Accessibility in Healthcare

From improving patient outcomes to driving advancements in the industry, the advantages of improved data accessibility are extensive and far-reaching. The following benefits are more easily achievable when barriers to accessing data are removed.

Enhancing Patient Care and Outcomes

Data is a core component of how clinicians diagnose, treat, and deliver patient care. However, to truly improve patient outcomes for individuals and the public at large, data must be accessible, accurate, and complete.
For individuals, when data is interoperable and shared, clinicians can review all aspects of a patient’s medical history and provide better-informed care options. Unfortunately, access issues can often cause complications if numerous disparate systems are used to create and store a patient’s information – even sometimes within the same healthcare systems.

Limitations involving poor medical data processing systems are the leading cause of medical errors. However, physicians who have a complete story can often diagnose faster, prescribe a more tailored treatment plan, and avoid adverse reactions to those treatments.

Representative data across a population can also improve patient care and outcomes in public health. For example, enriching datasets relating to physical health trends with SDOH information could lead to identifying patterns and causation. With these insights, public health officials could design new programs that address root behavioral causes, leading to improved care and outcomes.

Streamlining Clinical Trials

Clinical trials depend on rich datasets for cohort selection but identifying the right patient population is often an arduous task. RWD can be used to streamline cohort selection and broaden the candidate pool for trial recruitment. In addition, research supports that complementing randomized clinical trials (RCTs) with RWD can lead to improved trial efficiency and lowered costs.

This descriptive study found a correlation between access to EHR data and the ability to accelerate clinical trials.

Facilitating Research and Collaboration

Research and collaboration among institutions is another area that can thrive with improved access to healthcare data. Research projects use data as a foundation, and the data must be comprehensive in global public health.

RWD can help organizations to investigate emerging health issues, pandemics, epidemics, emergency preparedness, and the development of new treatments. Researchers can use various clinical data collection methods to clarify probabilities, identify patterns, understand patient outcomes, and find correlations between environmental, physical, and emotional factors and disease.

Improving Population Health Management and Making Globally Representative Decisions

Access to data is critical for population health management to serve all populations. If the data sources only provide insights for a subset, they don’t evenly apply.
As noted, most clinical data available for research and development is heavily US or European-biased, which leaves out a considerable number of people.
Improving access to healthcare data that represents all groups will allow for more comprehensive modeling of the risks within a population. This analysis can lead to better forecasting and prevention of disease outbreaks. Additionally, it can inform the programs deployed in a region so they are more relevant and beneficial.

Driving Innovation and Advancements in Healthcare

Finally, improved data accessibility in healthcare drives innovation and progress in the field. The increasing sophistication through newer technologies like machine learning models has been instrumental in developing new treatments, medications, policies, processes, and more.

Given the challenges facing the global healthcare ecosystem, this level of innovation is both timely and necessary. Aging populations, climate change effects, and the threat of pandemic-level infectious diseases, among other issues, create significant strain on global health resources. So much so that this topic was highlighted at a recent Alliance of Academic Health Centers International (AAHCI) Global Innovation Forum.

Experts stated that intrinsic and extrinsic factors affect innovation and that academic medicine enables innovation, but not always equitably. For example,  a US patient may receive the latest therapies for the treatment of sickle-cell disease,  but those patients in Africa with the same genetic condition may not have access to similar innovations.

Although there are more factors at play here than simply data accessibility, it’s worth considering whether or not this treatment might be more widely distributed if data that represents more populations was readily available and used to develop next-generation therapies.

Where Does Data Accessibility Fall Short?

Given the greater role that artificial intelligence is playing in drug development, more and better data will be required to power machine learning algorithms to develop meaningful insights. However, there are several significant challenges that currently exist with broadening accessibility of data for research.

Privacy and Security Concerns

Patient data is subject to compliance regulations throughout the world. People have become concerned about breaches as hackers continue to target healthcare.
As a result, the creation, use, sharing, and storage of patient data must adhere to stringent rules and guidelines. This can cause issues with interoperability, and some organizations are reluctant to share anything, even knowing the potential benefits.

Interoperability Issues

There is no global standard for representing healthcare data, and even within a single country, multiple standards often prevail.
For rich and diverse datasets, data must come from many different sources, and aggregating data from these sources (or federated technology) can then be a highly complex process. This is especially true for public health data, as concluded by a
The authors found that a lack of standards and the abundance of unique information systems pose challenges to the interoperability and sharing of public health data. To overcome these challenges, the global health ecosystem must move toward data standardization and ensure that data is clean, accurate, and complete.

Legal and Regulatory Fragmentation

As with privacy and security concerns, country-specific legal and regulatory requirements are not always data accessibility friendly. While regulations are necessary to protect patients, they can create compliance issues for public health entities.
It is imperative that any data platform looking to provide access to global RWD understands and abides by these legal and regulatory requirements. The obstacles they pose are real but not insurmountable. Finding a balanced approach that ensures privacy first compliance while enabling access to a diverse, globally representative dataset is key.

Technological Limitations

Another challenge can sometimes be the technology itself. Many different healthcare platforms create and use data, which exacerbates the standardization issue. Within a given hospital, clinicians utilize multiple procedures to deliver patient care in a given day, from nondigitized tools to handwritten notes, EHR systems, and related legacy (sometimes homegrown) applications. In some of the less developed healthcare systems, adoption of EHR systems is non-existent; paper charts prevail and are often incomplete and sometimes illegible.
Even when systems do exist, they may not be built using modern architecture principles, causing integration issues with newer solutions. Additionally, some commercial systems providers make it difficult to extract data consistently or charge high fees to do so.

Data Ownership and Control

Who owns the data? And who controls it? These are two questions that affect everything about healthcare data and its use.
The institutions that generate patient histories, labs, procedures, treatments, and outcomes own the data, as it lives in their systems, but patients also have ownership. There’s no simple answer to the ownership questions, and healthcare organizations must abide by the rules and regulations that govern the data in their resident country.
As more countries move toward empowering patients and requiring their access to their personal records, this will become an even more complex issue.

The Future of Data Accessibility in Healthcare: Better Clinical Outcomes

The future of data accessibility must address the challenges defined above. Several key areas will guide what happens next, including:

  • Accessibility must consider more than the availability of existing data sets. It must also seek to improve the diversity and representativeness of the data at a global scale.
  • Data standardization will be critical for mitigating roadblocks to aggregating data across the globe to use in public health initiatives.
  • Healthcare and regulatory bodies must partner to define and possibly standardize privacy, security, and confidentiality guidelines.
Taking steps to resolve the issues and expand access will drive the future of healthcare data with the potential for improving global health outcomes.

We Are Moving Data Accessibility Forward

Opportunities, and challenges are present in global healthcare data accessibility. The benefits are immense and can positively impact world health. While challenges persist, they are not insurmountable.

Through the Syntium platform, Syndesis Health is improving access to RWD from underrepresented regions in an effort to advance global data accessibility. Learn more about how we’re working with healthcare and life sciences organizations to improve global health equity and facilitate better clinical outcomes by meeting with us.

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