Over the past decade, biopharmaceutical researchers have made significant progress in discovering innovative drugs. Once-deadly diseases, like melanoma, multiple sclerosis, and malaria, have become more understood and manageable.
Despite these breakthroughs, many life sciences companies search for a more comprehensive approach to discovering drugs and predicting which therapies will be effective for which groups of patients. The industry faces challenges in collecting and organizing high-quality, abundant human data that allows researchers to form hypotheses and, by using technology, discover trends and patterns invisible to the human eye.
Effective health data management enables pharma to unravel disease biology and advance prevention and treatment. That is why the Viseven team wants to share insights on the importance of health data and best practices for managing it correctly.
What Is Health Data?
Before we dive into the core aspects of health data management, let’s discuss the key types of health data. Typically, data falls into two main categories:
- Traditional data includes information gathered through research, healthcare providers (HCPs), healthcare organizations, and public health agencies.
- Expanded data encompasses socioeconomic, behavioral, environmental, and lifestyle factors. For instance, health data might include information about an individual’s education, wearable sensor readings, or lifestyle habits.
Source: Deloitte
Combining these types of health data allows pharmaceutical companies to understand a person’s health and genetic profile better, thereby improving the quality of healthcare products and services.
What Is Health Data Management?
Pharma companies deal with enormous amounts of health data. According to the Deloitte report, the volume of health data rose by 40% between 2010 and 2020 and is predicted to climb even higher by 23% by 2025. Yet an overwhelming 97% of data generated by healthcare facilities stays unused.
To shake the dust off the dormant data, companies must learn how to manage it to their advantage. Health data management presupposes systematically collecting and organizing data on a digital platform and preparing it for analysis to support drug research and development while safeguarding privacy. Key examples of health data management include electronic health records (EHRs), health data platforms, and even healthcare providers’ notes scanned into a digital library.
Why Does It Matter?
Taming large volumes of health data offers numerous benefits. Let’s explore some of them:
Accurate differentiation between disease stages
By tracking data from healthy individuals, those newly diagnosed, and those who have been living with a condition for some time, researchers can more accurately differentiate between disease stages. This, in turn, helps to better predict the onset of diseases and contributes to a finer understanding of disease progression.
Holistic care
Collecting and organizing large-scale human data can reveal how different factors, such as nutrition, behavior, or medical devices, can be integrated and sequenced to address specific patient conditions holistically. Moreover, data collected from different health systems ensure a bird-eye view of the patient’s state and a more comprehensive approach to treatment.
Improved disease biology understanding
High-quality data enables pharmaceutical companies to understand disease biology better and develop hypotheses for conditions that were previously difficult to treat and manage, such as dementia. It also helps meet the specific needs of patients with manageable conditions, like therapy complications.
Personalized medicine
Data analytics is an indispensable part of effective data management. Thanks to data insights, personalized medicine is no longer science fiction, and the days of trial-and-error medicine are numbered.
Typically, HCPs establish a most likely diagnosis and prescribe a medication, often a blockbuster drug, and choose dosage based on the patient’s body weight. If it does not help, they change the dosage or choose another drug if possible. In contrast, personalized medicine enables providers to identify a disease at the outset and determine the right drug and dosage based on the patient’s unique physiology and ability to metabolize drugs.
Better research & development process
The research and development process can become less challenging and risky. More complete real-life evidence would help generate and validate hypotheses more quickly, accelerating the release of new drugs.
Health Data Management Challenges
The past decades have been truly transformative for the pharmaceutical industry, shifting from paper-based data organization to digitalization. However, even with most data now digitized, it has become hard to consolidate and manage its ever-growing volume. Here are some of the key challenges the industry faces:
Fragmented data
Health data can live in various databases and spreadsheets and come in different formats, such as text documents, video, and audio files. This data is often duplicated and collected by hundreds of different HCPs, pharmaceutical companies, insurance bodies, public health agencies, and others.
This results in silos within healthcare systems, which hinders artificial intelligence (AI) algorithms from correctly analyzing the data and leads to a “one size fits all” approach to patient treatment. Life sciences brands need a single source of truth to ensure the AI model’s outputs are complete and accurate.
Data collection costs
From the first challenge, it is clear that consolidating all data onto one platform is crucial. The issue is that the more data you collect, the more expensive the solution becomes.
In the context of drug discovery, it may cost up to $27,200 to gather data on a single person. This sum can skyrocket to $27.2 billion with at least one million participants. However, these costs are justified. This platform has the potential to generate around $25 billion annually within 20-25 years, even with slow innovation.
Ever-changing data
Heraclitus, a Greek philosopher, once said, “Nothing is constant but change,” and we could not agree more, especially when it comes to health data management. Patients undergo treatments, surgeries, and various tests, causing their health states to change and their therapy courses to evolve. They relocate and change their HCPs, and new medical treatments and screening methods are developed, producing volumes of new data.
These changes in data necessitate innovative data management methods. Companies need to find effective ways for data standardization to keep it relevant and updated easily over time.
Legal compliance
As hacker attacks become more frequent, it is crucial for life sciences businesses to focus on legal compliance to improve their data security measures. Evidence shows that last year was particularly bad for healthcare organizations, with a staggering 239% increase in attacks. Unfortunately, it is not just the number of hacker attacks that has skyrocketed; these data breaches are also becoming more serious and costly.
Pharmaceutical companies must comply with stringent government regulations, like HIPAA (Health Insurance Portability and Accountability Act) in the United States or GDPR (General Data Protection Regulation) in Europe, to safeguard sensitive data from hackers’ eyes:
- HIPAA provides a structure to ensure the privacy of patients’ medical records and other protected health information. It outlines how medical data should be used, protected, and disclosed.
- GDPR treats healthcare data as a type of personal information and offers guidelines for its protection. This regulation sets rules for accessing, editing, and deleting health data and requires healthcare organizations to have legal permission to process it. Notably, the GDPR applies to many industries, not just healthcare.
Best Practices for Healthcare Data Management
The question now is how to overcome these challenges and fully leverage your health data. Here are some tips to help you on your journey:
Prioritize data governance
Effective health data management begins with robust governance. This involves overseeing processes, employee conduct, and technology use throughout its lifecycle so that health data remains accurate, secure, and usable.
With proper data governance, internal policies are established to govern data collection, usage, and disposal. Equally important is to comply with external standards set by government agencies and industry bodies.
However, none of these policies can be effectively implemented without a culture of transparency. Holding the entire organization accountable for maintaining ethical standards and meeting regulatory requirements becomes a task with a caveat when stakeholders are unaware of organizational processes. Transparency is key to maintaining a sense of accountability among employees, continuous improvement, and role understanding.
Merge health data management systems
Patient care needs to be holistic, meaning all healthcare providers should work together to deliver the best possible outcomes. When data is siloed, professionals are left in the dark about what interventions and therapies their colleagues have already prescribed. Even though each provider’s treatment might make sense on its own, combining different approaches without coordination can harm the patient. It is like they say, “The operation was successful, but the patient died.”
You need data interoperability to ensure all efforts are well-coordinated. This means different systems can interact seamlessly and exchange data quickly. As a result, pharmaceutical companies can gather and process large datasets from various platforms, speeding up drug discovery, ensuring patient safety, facilitating clinical trials, and improving the organization’s efficiency. Additionally, efficient data exchange minimizes redundant procedures and processes, leading to substantial cost savings.
Therefore, you should also pour resources into interoperable systems that “talk” to each other, regardless of the vendor. Do not forget to deliver proper training for all stakeholders on how to use the technology correctly. It is also crucial to follow data exchange frameworks and regulations to ensure data transmission is fully secure.
Ensure data privacy
Neglect this aspect of data management, and you risk significant financial trouble and irreparable reputational damage. Unfortunately, your employees remain one of the main weapons in hackers’ hands. This does not necessarily mean your workers are directly causing data leaks, but their negligence and lack of data security education do. Security awareness training enables the organization to make smart decisions when storing and transmitting patient data.
Access management is another important consideration in ensuring data security. It is essential to track who accesses which information to prevent your data from leaking into unauthorized hands. Multi-factor authentication is the way to go to protect your data with various validation methods. These include anything from a PIN number, password, facial recognition, or even eye scanning.
Data encryption is also one of the most effective ways to minimize the risk of data breaches. Even if hackers gain access to sensitive information, they would not be able to decipher or make sense of it. Industry regulations like HIPAA provide guidelines on implementing encryption methods, but they allow healthcare professionals and other stakeholders to select measures that align with their unique business goals and needs.
Facilitate data storage and retrieval
Effective data storage and retrieval processes enable life sciences companies to boost operational efficiency, accelerate decision-making, and improve research and development efforts. This allows them to access data quickly and reduce downtime, speeding up clinical trials and the release of drugs to the market. Whether you advocate for moving to the cloud or prefer more traditional on-premises solutions, your storage solution should be secure, scalable, and cost-effective.
The next step is to tag all your data for efficient retrieval. For example, we developed a content experience platform, eWizard, which uses AI to break down information into logical chunks and tag them automatically. This helps our clients navigate the vast amounts of data generated during marketing activities more effectively. We also recommend making regular backups to ensure no data sets are lost.
eWizard auto-tagging engine
Turn raw health data into insights
There are three types of data analytics: descriptive, predictive, and prescriptive. Descriptive analytics lets you summarize historical data to identify patterns and trends that would otherwise be lost in the vast sea of information. For example, descriptive analytics lets scientists trace the connection between disease pathology and environmental factors.
When it comes to predictive analytics, machine learning can be used to anticipate future outcomes. For instance, researchers can utilize big data to predict the onset of a condition or pandemic. This could be an invaluable advantage, especially after what we have learned from COVID-19.
Prescriptive analytics, on the other hand, provides actionable insights to help you make the most effective and rational decisions. For example, with medical data on when and where a disease will occur, pharmaceutical companies can proactively develop and stockpile the right medications.
Make sure your employees can easily digest health data and quickly spot patterns and trends. Use clear visualizations like diagrams, charts, or graphs. Interactive dashboards are also great, especially if you want to customize the visuals to different job roles.
Final Thoughts
Given the complex nature of disease biology, researchers need advanced technology to uncover patterns that explain various conditions. Proper medical data analysis is crucial, but it is also essential to securely store, collect, retrieve, and visualize information. This blog post is designed to provide an overview of the core aspects of healthcare data management to help you start and navigate your own data management journey.
Trust the numbers and hands-on expertise. With over 14 years of experience in developing digital solutions for health data management, the Viseven team creates robust native, hybrid, and web apps that automate health data collection and organization, improve health outcomes, and personalize patient care. Our solutions comply with GDPR, HIPAA, and numerous other industry standards. We also pride ourselves on a 95% client satisfaction rate, with 80% of our clients coming from referrals.
If you are looking for a reliable service provider, do not hesitate to contact our experts.
