Big Data has significantly impacted the healthcare industry. According to Bloomberg, 2021 will be the year of big data collaborations and analysis through artificial intelligence. Another trend is the development of patient health information portability and telehealth usage.
Let’s cut to the chase: why is data collection important? Because data collection feeds Big Data, and Big Data helps identify repeatable patterns and turn high volumes of data into a complex system that can help make the right decisions and predictions for patient treatment.
Why do most medical organizations still manage all their information in paper forms (and moreover, keep these papers in different places) and don’t use online data collection solutions? Well, there is no single answer to this question. In this patient data collection guide, we will outline the major challenges of data collection in healthcare and provide methods to help medical organizations start gathering and analyzing information more efficiently.
Data Analysis Guide: How to Start Collect Patient Data
Depending on the measure, patient data can be collected in different ways: medical records, patient surveys, administrative databases, surveys, comments from individual patients, etc. The gathered information can also be stored in various formats.
Some medical institutions already use electronic health records to store data. Others may find it challenging and still keep all the information in an Excel spreadsheet or one-off Access database. Such self-created data collection tools are not convenient for collaboration. Why? Entering data results in new data silos and can lead to chaos in the future. Also, spreadsheets are usually shared between different people, which can lead to security problems and data breaches.
So how exactly can this data be gathered? There are various data collection tools in healthcare that help professionals analyze and accumulate data from several sources. However, don’t fall into the trap of automating everything. Start gathering and collecting data step-by-step. First of all, decide your main goals and challenges and choose the healthcare data collection methods accordingly.
Usually, the goals are the following:
- Reduce workloads and increase profit;
- Personalize treatment;
- Improve collaboration between patients and doctors;
- Forecast and predict healthcare issues;
- Identify and intervene on high-risk and high-cost patients.
After defining your goal, decide the type of data you will collect.
Administrative Data
It includes general data about the types of services the hospital provides, charges for those services, diagnosis and procedure codes, amounts billed, and service locations.
Patient Medical Records
Patient treatment history, symptoms, demographics, vital signs, diagnoses, medications, treatment plans, progress notes, problems, immunization dates, allergies, radiology images, and laboratory and test results.
Patient Surveys
Surveys by mail, by telephone, or via the Internet aimed at measuring the patient’s overall satisfaction. Depending on your goals, the questions may be related to staff responsiveness, clinician communication, technical skill, and hospital environment.
Individual Patient Comments
It includes comments about the doctors, the performance of the health plans, and the general experience. Such information usually generates more insights as it’s based on emotions and is more personalized. Combine the formal data with patient comments to get a holistic view of the available information, and build more strategic decisions in the future.
To gather this data, you need to implement a variety of collection processes that may include:
- Patient forms for collecting information from new patients
- Contact update forms
- HIPAA release forms
- Medical history forms
- Informed consent forms
Also, remember the following digital transformation challenges when it comes to data collection.
- Fragmented data from a different number of sources and in different formats
- Ever-changing data that makes it challenging to keep health care data clean and current
- Privacy and security regulations
- Lack of training in data collection
When you have decided what data to collect, choose the appropriate solution, taking into account the services your company provides, the number of doctors and patients, existing tools your organization uses, and your budget.
Most Popular Tools for Data Structure in Healthcare Industry and Storage Organization Examples
Here are the three most popular tools for data collection in healthcare your organization can implement:
- CRM (Customer relationship management)
- EHR (Electronic Health Record)
- Mobile applications and software
“As for healthcare, the deep data learning helps analyze the large volume of data, divide it into understandable chunks, and make the treatment more personalized. That’s why we believe that doctors and medicine will benefit from custom data collection tools.”
— Vlad Medvedovsky CEO at Proxet, a custom software development solutions company.
CRM System
CRM is a customer relationship management system that generates and handles information, provides reports, and analyzes various issues. CRM allows the following:
- segmenting patients to provide more personalized treatment,
- keep track of all communications in one place,
- get a complete view of all your customers with data generated from multiple sources (such as EHR, patient satisfaction surveys, etc.)
Usually, a healthcare CRM should have the following features:
- Reports and dashboards
- Direct Mail Campaigns
- Integration with your existing tools
- Customer Management
- Operation on multiple platforms
However, healthcare CRM cannot function by itself and needs to be integrated with other sources it will pull data from. For better data collection, we recommend integrating your CRM with EHR. Thus, it will scan the essential data stored in the EHR and transfer it to either a patient or a caregiver to provide more personalized care.
EHR
EHR is an electronic health record system that accumulates and analyzes personal patient information for generating insights and giving predictions. It stores both nursing data collection and medical data collection.
EHRs facilitate the identification of individual patients in clinical workflows. Patient identifiers consist of the patient’s full name, date of birth, contact information, name and contact information of the next of kin, emergency contact information, and other personal information. Besides, EHRs generate a unique patient ID (i.e., medical record number) used within the care system to identify a specific patient.
Most Important is Data Security: How to Protect Your Data
Under federal regulation, you are responsible for protecting the confidentiality of personal health information that can be accessed through your electronic health record (EHR) system. The data breaches in your organization can damage your reputation and lead to financial losses and harm to your patients.
“Security has always been a people issue," he told Healthcare IT News. "The toughest security problem is getting people to understand. It's the same issue we had five years ago; it's going to be the same issue five years from now.”
— Jigar Kadakia, CISO at Mass General Brigham
Here is how you can ensure that the data you collect stays safe:
- Implement two-factor authentication and other methods in addition to simple usernames and passwords.
- Monitor and log all access attempts.
- Make healthcare workers access the healthcare service’s networks.
- Isolate devices that connect to healthcare networks as part of the growing Internet of Things (IoT).
- Lock down all remote-access connections to the networks with VPNs and other secure communication technologies.
- Adopt role-based access control (RBAC) so the employees will only get access to the data sources they need for their jobs.
- Collaborate with partners.
- Encrypt all sensitive data.
“We have a very active intelligence program. We don't rely just on our own monitoring. I work with a lot of third parties. I work with our government agencies, our own healthcare agencies, other financial agencies to understand where the real threats are. You can't rely on your own systems. You have to collaborate with other partners.”
— Cris Elwell, CISO at Seattle Children’s Hospital
Examples of Software for Patient Data Collection
With the current revolution in machine learning, image analysis, and natural language processing, there are many big data tools available to organize and process data.
“With the barriers surrounding siloed electronic medical records beginning to fall, with genetic and other ‘omics’ data becoming cheaper and more widespread, and with powerful big data analytics tools in hand, we’re now on the cusp of realizing the dream of truly personalized medicine.”
— Mayur Saxena, Co-Founder and CEO of Droice Labs, on AI to match patients to therapies in his column in Forbes.
AI intelligence is already used in healthcare for image analysis. Here are examples of the most popular AI/ML software for patient data collection to transform the industry.
Enlitic
Enlitic provides deep learning medical tools to streamline radiology diagnoses. The platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs. Their mission is to bridge human and artificial intelligence to advance medical diagnostics to improve patient outcomes around the world.
Benevolent
Benevolent uses artificial intelligence to produce a better target selection and provide previously undiscovered insights through deep learning to provide more personalized treatment. The platform is built on powerful data foundations and technology that empowers scientists to decipher human biology and find new ways to treat diseases.
Tempus
Tempus is a massive data library for personalized health. The company is developing AI tools for collecting and analyzing data from genetic sequencing to image recognition that will give physicians better insights when it comes to treatments.
Medicine is one of the industries that fully embrace artificial intelligence/machine learning (AI/ML) algorithms, so this sector's opportunities are endless.
Proxet has significant expertise in implementing data analysis and learning solutions in the healthcare sector. Our experienced product owners and developers will gladly assist you in deciding what data to collect and how to use it efficiently.
