In contrast with big data, where big volumes of information are processed in order to find out similar patterns, small data analyzes historical data of individuals and develops models for prediction or future treatment. Small data helps doctors manage patient care because it provides a lot of insights for individual patients, such as input on allergies, times for blood cultures, missed appointments, etc. Let’s see how small data is changing the healthcare industry today.
What is small data in healthcare
When it comes to healthcare, small data is more related to the patient and helps physicians provide more personalized treatment. Here are examples of small data to better understand what is small data in healthcare.
- Missed clinic appointments
- Allergic reactions to drugs
- Operating room turnover times
- Timeliness of blood cultures for septic patients
- Correct diagnosis of ADHD in teenagers
Big data in healthcare can’t tell you that Mrs. Kates was admitted to the ER twice last week, but that’s what small data is capable of.
“Small data is being generated continuously on our mobile phones and through our online activities: walking and location patterns, as well as shopping, communicating, and web surfing. It is the various data traces we each generate every day, just by living our day-to-day routine: checking email, taking the bus to work, going grocery shopping, walking home, and more.”— Deborah Estrin, Computer Science at Cornell Tech and Healthcare Policy and Research at Weill Cornell Medicine.
To improve healthcare analytics, clinics and healthcare institutions should break big data into smaller parts to precisely understand the available information.
As you can see from the image above, the circle of medical knowledge begins and ends with the individual patient. That’s why if providers focus more on the small chunks of data, it can reveal a lot of actionable insights about their patients. So what is small data?
“Small data will tell us more than all of the big data we have now.”— Abbas Mooraj, Optum Vice President (speaking at the HIMSS Big Data and Analytics Forum Monday).
Difference Between Small and Big Data in Healthcare
Let’s see the difference between small data vs big data on the example of electronic health records. Check out which questions both of them help clarify:
Big data
- What can be the effect of immunization programs?
- Where do some of the healthiest people in the world live?
- Are there any generic factors to identify a disease?
Small data
- Is my child’s immunity to diseases taken care of?
- Is my diabetes medication working as expected?
- Am I susceptible to X disease?
If the systems will be able to leverage individual health information, both physicians and patients will take the most out of digital health technologies. The benefits of small data are:
- It’s punctual and transactional in nature
Usually, the lag between interpreting information and acting on it is very small. For example, “My patient’s blood pressure has increased today”.
- It’s purposeful
The information displayed about the patient directly influences the patient goals of therapy and the desired outcome. For example, “Is the plan of care comprehensible for my patient?”
- It’s prognostic
Small data can predict future risks and poor outcomes. For example, it can foresee different harmful environmental factors that can increase the risk of falling.
- It occurs everywhere
Small data is very point-of-living as it can occur everywhere the patient is in contact with a multidisciplinary team. For example, it can show that the patient was short of breath when delivering their products home. It helps look at the problems from a smaller perspective and therefore find a more effective and personalized solution, and the difference between big data and small data can become crucial.
Importance of Small Data in Healthcare
According to statistics, as healthcare forges a path forward in 2022, the focus will be on building more resilient supply chains, specifically using supply-chain data to drive the transparency and insights needed to help the industry be prepared the next time a crisis strikes.
Clinicians prefer small data. Small Data approaches always consider the context and provide doctors with a better understanding of relations of health and well-being issues. Personal health data can show a lot of disparities in the quality of life. For example, personal well-being small data can show how physiological stress is connected to societal norms and pressures instead of being related to individual weaknesses.
Small data analysis is all about measuring our life. That’s because it’s impossible to measure things in the doctor’s office at a time of a clinical visit, so doctors take patient histories all the time. Small Data helps collect, analyze and apply personal health data to give individuals a chance to get more meanings and insights on the information they possess. Actually, there is no big data without small data, and each medical act is the intersection between the small and big data.
Healthcare Small Data Analytics Use Cases
The role of small data analytics in healthcare is huge. There are four types of data analysis:
- Descriptive analysis, which examines and describes something that’s already happened
- Diagnostic analysis, which seeks to understand the cause of an event
- Predictive analysis, which explores historical data, past trends, and assumptions to answer questions about the future
- Prescriptive analysis, which identifies specific actions an individual or organization can take to reach future outcomes or goals
Small data is one of the healthcare trends that can be captured and analyzed by improving enterprise cloud and intuitive mobile interfaces. For example, a doctor can note changes in condition that may signal a worsening clinical state (like an increase in weight of a heart failure patient or new home hazards that might trigger a fall) and send alerts to the appropriate team member for quick action.
After that, different activities of daily living, psychosocial issues, caregiver and environmental factors, and measurements of a patient’s understanding of her care plan and medications are some of the key dimensions to capture and analyze healthcare data sets definition.
That’s what small data is all about: personalized information and all dimensions of care that reflect the patient’s health state.
“Small data is important in any industry, but especially in medicine. Before choosing an organization for developing custom software development solutions for your healthcare business, it’s important to talk with your partner about how small data can be analyzed to make this solution more personalized.”— Vlad Medvedovsky, CEO at Proxet (ex - Rails Reactor) - a custom software development company.
There are some small data problems in healthcare. Healthcare providers should move away from big data phenomena and look for tiny solutions to create a larger puzzle from little pieces of information. Healthcare providers have big issues that can be solved only with little solutions with the help of moving with small steps.
Leveraging technology is not always so simple, though it may seem effective. There are some challenges facing providers who want to establish a big data platform, such as EHRs that are not designed to be a “scientific repository to drive better care — they’re designed to optimize billing.
EHRs do not channel external data, but the problem is not only in EHRs themselves. EHR data is usually hierarchical or sequential when big data is different as it can be represented from file types to schematics. Finding ways to really have all of this data work together is one of healthcare providers’ biggest challenges today.
Approaches to Rising Small Data in Healthcare Technology
One of the best approaches to gathering small data in healthcare is to manage our health analytics in a passive way or measure it from tasks we are already doing.
How is small data used in healthcare? For example, Ginger.io on-demand mental healthcare platform assesses patient behavior through sensor data collected through an app or smartphone. It notifies your doctor if it detects behavior changes that match clinical indications of depression or other diseases, with no action required by the patient.
Lifestyle and interventional data can be used to support and gather genetic data along with the monitoring function to track it. For example, people with diabetes can monitor their diet and exercise programs and gather data to help doctors decide if they need different medications or a new diet with training programs.
There are also many small data in healthcare examples, such as smartphones, that improve population health and help connect the dots between the small data that can maximize an individual's personal care and the big data that can uncover more global issues.
However, one of the challenges for technology experts is to ensure privacy when tracking biometrics and other information. Also, not all physicians are ready to use such tools and health programs as they state that tracking additional datasets may be too challenging for them. So it’s a matter of time whether providers will make the most out of the power of such tools.
If you are planning to improve the performance of your clinic or hospital now, we at Proxet will gladly help you develop any kind of healthcare software and help you get the most out of small data, as well as big data healthcare.
Our team has significant expertise in implementing custom software development solutions in big data and healthcare analytics. Our experienced product owners and developers will gladly assist you in deciding what data to collect and how to use it efficiently. Contact us whenever you are ready to implement the newest technologies in your business!
