2323
Blog

Data-Driven Healthcare: The Ultimate Guide

What is data-driven healthcare?

Data has been touted as the new oil, the new black gold that’s set to change sector economies and transform how organizations engage with customers, markets and solutions. Every industry is looking to mine its depths and tug out those crucial threads of insight that will allow for them to improve performance and outcomes, but the healthcare industry has specific nuances that make the data even more valuable.

Data-driven healthcare is the use of data and advanced technologies to make smarter, faster and more informed decisions in medicine. By analyzing vast amounts of information – like patient records, imaging and even real-time data from wearables – it helps healthcare providers deliver more personalized care, improve outcomes and streamline operations.

Data-driven healthcare not only streamlines data management but improves systems for patients, practitioners and institutions.

7 types of healthcare data

Healthcare relies on a diverse range of data types to ensure effective patient care, operational efficiency, and medical advancements. Each type of data provides unique insights, from individual patient records to broader trends in population health, driving the innovation and precision of data-driven healthcare.

  1. Electronic Health Records (EHR): EHRs are digital versions of patients’ medical records that provide comprehensive, real-time information on their medical history, treatments, test results and more. They facilitate seamless communication between healthcare providers and improve care coordination.
  2. Administrative Data: This includes non-clinical information such as hospital staffing, facility usage and billing details. Administrative data supports efficient healthcare operations and resource allocation.
  3. Claims Data: Claims data are records of billing and reimbursement interactions between healthcare providers and insurance companies. They offer insights into healthcare utilization, costs and treatment patterns.
  4. Patient/Disease Registries: These are centralized databases that track specific patient groups or diseases over time, providing valuable data for monitoring trends, outcomes and the effectiveness of interventions.
  5. Health Surveys: Health surveys gather self-reported data from individuals or populations about their health status, behaviors and access to care. They are vital for assessing public health and designing targeted interventions.
  6. Clinical Trial Data: This data comes from controlled studies testing the safety and effectiveness of treatments, drugs or medical devices. Clinical trial data drives evidence-based medicine and regulatory approvals.
  7. Genomic Data: Genomic data refers to information about an individual’s genetic makeup, enabling precision medicine by tailoring treatments based on genetic predispositions and mutations.

4 types of data analytics in healthcare

When looking at data-driven healthcare, there are 4 main types of data analytics, each contributing to better decision-making and improved patient outcomes.

  • Descriptive Analytics
    • What it does: Provides insights into past events by analyzing historical data.
    • Example in data-driven healthcare: Tracking patient admissions, discharge rates and disease prevalence over time.
    • Value: Helps healthcare organizations understand what has happened and identify trends, such as seasonal patterns in illnesses.
  • Diagnostic Analytics
    • What it does: Explains why specific outcomes occurred by identifying root causes.
    • Example in data-driven healthcare: Investigating why a particular group of patients experienced adverse reactions to a treatment.
    • Value: Enables healthcare providers to uncover contributing factors and help improve care strategies.
  • Predictive Analytics
    • What it does: Forecasts future outcomes based on patterns in historical and real-time data.
    • Example in data-driven healthcare: Predicting the likelihood of hospital readmissions or identifying patients at risk for developing chronic conditions.
    • Value: Allows for proactive care and resource planning, helping reduce costs and improve patient outcomes.
  • Prescriptive Analytics
    • What it does: Recommends specific actions or solutions based on predictive data and optimization models.
    • Example in data-driven healthcare: Suggesting optimal treatment plans for patients with complex medical conditions based on data-driven insights.
    • Value: Enhances decision-making by offering actionable recommendations tailored to individual needs.

Structured vs. Unstructured data in healthcare

In healthcare, structured data refers to highly organized information that is easily searchable and stored in predefined formats, such as numerical values or coded fields in EHRs. Examples include patient demographics, lab results and billing codes.

Unstructured data, on the other hand, consists of free-form information that lacks a specific format, making it harder to analyze directly. Examples include physician notes, medical images and audio recordings. While unstructured data holds valuable insights, it requires advanced technologies like natural language processing (NLP) or AI to extract and utilize effectively.

The three pillars of data-driven healthcare

1.) Access to patient data

A recent report undertaken by Pew Charitable Trusts identified the need for improvements in how patient data is used and accessed. The report highlighted how important it has become for patients to gain deeper control over their own data and for the flow of their information to become more accessible and seamless, enabling data-driven patient insights that enhance care.

There are challenges when patients visit different practitioners and institutions – data isn’t readily available and can even limit patient care. While there are clear privacy and data protection considerations that have to be put in place before data can be so easily moved between medical practitioners and healthcare facilities, the benefits to patient and practitioner are clear. Patients with chronic health conditions would really see the value of visible data and access as they would be able to share information, when needed, while reducing their own admin and cost burden. 

In some instances, patients are expected to pay for their information or are refused access to their medical information. This puts them in a position where they have to either fork out funds for further testing or start the process over. It’s a complex situation but, ultimately, the patient is the person who creates the data so they should have the right to access that data.

That said, the information held by medical institutions is often of little use to patients as they don’t have the medical knowledge to decipher the actual data. Which is why data-driven healthcare would ultimately require that data be as easy to understand for the patient as for the practitioner as this would not only improve how patients approach their own care, but how they engage with medical professionals. 

This level of data sharing and insight can potentially create data ecosystems that are designed to ensure that healthcare is powered by the right information at the right time. Data ecosystems should be designed to make information usable and relevant. This can then be combined with legislation, regulation and the institution’s willingness to transform approaches to data, to define the parameters of data-driven healthcare. 

2.) Privacy and security 

Wherever there is data, there’s someone trying to gain access to that data. Cybercrime has become a phenomenal risk over the past few years and has only increased in intensity as cybercrime becomes more profitable and capable. The war for data is one of attrition, fought in regulation and legislation and on the front lines of system and security. This is further complicated within the medical sector as data here has to remain private and risk must be mitigated as effectively as possible. 

The GDPR in Europe set the gold standard for the protection of personal information and countries worldwide are looking to how they can follow suit. There’s a growing awareness around the management and collection of personal data and this will have a fundamental impact on the healthcare sector, particularly as it moves towards more data-driven systems and approaches. 

This doesn’t mean that data-driven healthcare is hobbled before it can start. However, quite the opposite. It means that healthcare is in a position to learn from other industries how to set  best practices and mistakes to create data ecosystems that are ready for regulation and capable of ensuring both privacy and security while maintaining the highest possible levels of patient care.

3.) Innovation

Machine learning, automation, deep learning, artificial intelligence (AI), neural networks and intelligent algorithms – these are the drums of innovation that are beating within the healthcare industry right now. The data that these systems and solutions create is extraordinary, filling up vast virtual lakes with information that could potentially cure disease, manage treatments and improve patient care. These are equally the tools that can be used by the healthcare industry to dig deeply into this data and uncover insights that could change how a patient is treated or how a medical institution approaches patient care. 

A great example of this can be found in an article published by the American Journal of Managed Care that found how machine intelligence could be used to manage outbreaks of respiratory infections.  The result was that precision management through machine learning could ‘reduce the burden of outbreaks of respiratory infections’. Staying in the USA, Flatiron Health is a startup that gathers and analyzes data on cancer treatments in an attempt to refine cancer care and investment. The company pulls data from multiple sources to create insights that are of value to the industry.

Data-driven healthcare solutions

There are multiple startups exploring the potential of data-driven healthcare: Aidoc in Israel, developing intelligent solutions for radiology support; Concerto HealthAI using AI to predict and manage patient outcomes; Evidation Health that uses data to determine how everyday behavior can influence health outcomes; and Excientia that uses data and AI to speed up drug discovery and development.

Data-driven healthcare is squeezing into the cracks and crevices of the industry and opening up immense potential for improved health outcomes, practitioner capability and industry transformation.

Explore the Latest AI Insights, Trends and Research