Precision healthcare stems from precise diagnosis. Since allopathy is evidence-based, this precision boils down to the most accurate and up-to-date recording of symptoms and any minute data that could aid in strengthening the diagnosis.
Such data and inferences were earlier recorded and managed in paper-based files stored offline. Digitization paved the way for EHR data (Electronic Health Records) and made access to patient data more accessible to clinicians and physicians.
Despite the rise of EHR data, healthcare stakeholders still observed siloed data as patients visit not only multiple doctors but allied centers such as pharmacies, diagnostic centers, and more. Each interaction involves modification of existing data, which the next doctor or physician is supposed to know.
To streamline all this and give democratic access to patient care is the advent of longitudinal patient data. In this article, we will explore in-depth what this means, how it works, its benefits, challenges, and more.
What Is Longitudinal Patient Data?
In simplest terms, this is a compilation of an individual’s health-specific information over a prolonged period of time. It enables the registry and tracking of key aspects such as:
The health status of the individual
Disease progression and recovery
Risk factors
Treatment outcomes
Impact of interventions overtime
Other interactions: ER visits, prescriptions, medications, etc
The Impact Of Longitudinal Data In Healthcare Data Management
The quality of healthcare delivery is directly proportional to the availability of complete, accurate, and updated data. This lays the foundation for clinical and healthcare experts to offer personalized patient care. To break down the significant advantages of longitudinal data, here’s a quick list.
Optimize Patient Safety
Patient safety is of critical importance in healthcare. While the focus should be primarily on treating existing concerns or underlying diseases, attention should also be given to eliminating risks stemming from potential drug interactions, allergies, and adversaries.
Since longitudinal data documents a patient’s health journey over time, it gives stakeholders a bird’s eye view of previous interactions and observations. This enables them to modify their treatment procedures and protocols accordingly.
Efficient Management Of Chronic Illnesses
A report from 2023 reveals that the prevalence of chronic diseases in Indian urban areas is 29%. Stemming from lifestyle options, genetics, and diverse other factors, chronic diseases such as heart illnesses, diabetes, obesity, epilepsy, auto-immune conditions, and more are aplenty.
Such conditions require consistent tracking and monitoring of patient health. Medications and dosages depend invariably on recent observations and interventions. The availability of longitudinal data enables the optimization of treatment, better tracking of disease progression, and increased efficacy of treatment and drug approaches.
Streamline Healthcare Services
Apart from clinical advantages, there are several operational benefits as well. Longitudinal data allows for optimum resource utilization and streamlined healthcare services. Besides, it also helps patients save significant expenses by eliminating the need for redundant tests and reports.
Aid In Research & Clinical Trials
Longitudinal data goes beyond being a mere documentation procedure. It is a substantial foundation to
- Advanced and further medical research
- Help government bodies formulate appropriate health strategies
- Benefit epidemiological studies
- Provide data-rich repositories to conduct airtight clinical trials and more
- Enable the development of Healthcare AI systems
Challenges In Establishing Longitudinal Data
The benefits are promising and the impact is significant. However, this niche process is not without its challenges and bottlenecks.
Data Fragmentation
One of the primary concerns in making data across patient health journeys is the disparate EHR systems and modules. There is a stark disconnect among EHR systems deployed across different establishments, leading to scattered data.
Such fragmentation of data makes it difficult to instantaneously update and share patient data after each visit or interaction. With the rise of healthcare data privacy concerns and mandates such as GDPR and HIPAA, data de-identification and tokenization add another layer to already existing clinical and operational workflows.
Lack Of Standardized Data Quality And Structure
Overlapping with the aspect that multiple healthcare providers and establishments deploy distinct EHR systems, recording practices, file systems, formats, and terminologies differ from one record to another. This lack of standardization prevents instantaneous synchronization of patient data on the cloud or a centralized system.
Miscellaneous Factors
Apart from these, there are also chances that patients don’t completely reveal their symptoms or conditions. Such voluntary or involuntary withholding of information skews the entire process.
Another critical aspect revolves around the financial burden that comes with strengthening digital transformation initiatives and spending on technical and digital infrastructure to enable seamless data recording and tracking.
Longitudinal Data: The Transformative Asset In The Healthcare Space
The availability of adequate longitudinal patient data also allows healthcare experts to seek the assistance of niche technologies such as Healthcare AI systems. Through simulations and data science techniques such as prescriptive and predictive analytics, risks of developing diseases, predict illnesses 5 or 10 years ahead based on patient history and lifestyle choices and more can be studied and revealed.
With that said, it’s not just about making the technology available. It also boils down to the coming together of healthcare institutions and stakeholders to collaborate on healthcare data exchanges and train their staff and associates frequently on this to nurture this process on a more cultural level.
We believe that going forward, we will witness remarkable advancements in data exchanges and data recording methodologies.
And if you’re working on one such vision or building futuristic Healthcare AI models, our repository of ethically sourced de-identified patient data will serve as quality AI training datasets. We hope this blog gives you adequate clarity on what longitudinal data is. Get in touch with us today to explore the scope of data procurement for your AI visions.
