Healthcare and Data Science: How EHR and AI Can Go Hand-in-Hand

Healthcare and data science were made for each other. Accessible, informative patient data is critical to good care. When healthcare and data science are combined, the result is electronic health records (EHR). EHR use data science for the benefit of medical treatments and procedures. Additionally, healthcare provides the perfect input for artificial intelligence (AI) and machine learning (ML) algorithms. Streamlined workflows, improved database maintenance and more accurate result reporting are all within reach when AI/ML and EHR form a powerhouse technological team.

EHR explained

EHR are a digital compilation of every source of information on a patient, gathered into one database. They include medical history, data from treatments, diagnoses, prescriptions, immunization records, allergies to medications or foods, images generated by radiology and laboratory or test results.

EHR adoption into the healthcare industry came late in the 90s after HIPAA was enacted and signed in 1996. The procession of the integration was slow because of limited technology. In 2009 a significant boost came via the passage of the HITECH Act and the specification of the what, why, and how of EHR was implemented. The main purpose of the implementation of EHR is and was the expansion of patient care with the goal of increased treatment efficiency.

EHR essentially function in the same way as old paper charts but expand digitally into an interactive data science dashboard that updates in real time. Clinicians can examine the patient's medical information and perform a number of analytical functions.

The critical components of EHR are:

• Availability: EHR update and organize in real time for the use of data science functions like diagnostics and provide analytics for descriptive and predictive purposes. The data is available at all times and gets shared with all of the parties that are involved in caring for the patient (laboratories, specialty physicians, radiologists, pharmacies, hospitals, etc.)
• Security: Only authorized users can gain access and transform EHR information which is securely stored by elaborate access management protocol, data encryption, anonymization and routines for data loss protection.
• Optimization of workflow: EHR can automate routine functions in providers’ workflows. EHR automation can also process manage healthcare data processing regulations (HITECH, HIPAA, and PIPEDA) by initiating the protocol that is required during data processing.

How does AI/ML fit into EHR?

The availability of data is one of the most beneficial elements of EHR implementation into healthcare processes. Aside from information being readily available for medical experts any time it is needed, the way data is featured in EHR makes it a perfect fit for many ML-fueled data science operations.

ML is a great option for many elements of EHR such as:

Data mining: Gaining insights into medical practice requires a lot of data. Gathering this data takes a tremendous amount of time. The scope of data that medical facilities generate is increasing and that data’s complexity is expanding. This makes the use of ML algorithms a necessity for processing and analyzing information during data mining. The use of data mining in EHR revolves around two approaches that have differing scopes:

• Finding data: (about the patient and the treatment) In this instance, ML is used to collect pertinent information in the medical history and record of treatment to further aid in decision-making. Patient-centered data mining is utilized in the assessment of varying treatments and outcomes through the study of similar cases from the widened EHR database.
• Data extraction: In this case an ML app is used to gather pertinent data based on terms and outcomes across the EHR database. An example would be determining what medication proved to be active for specific ailments and the circumstances under which they were administered. The same tools can be used for exploratory research that are able to reshape available data to meet specific requirements such as examining lipid profiles from test result patterns.

Natural language processing (NLP): In one way or another natural language processing is used at some point in EHR operations. Most medical records are in text form combined with graphs and charts.

The main uses for NLP are:

• Document search: Used as part of broadened data mining operations and as a simple navigation tool used internally. The system utilizes a named-entity recognition that is trained on a specific set of terms and designations related to various tests and medical exams. The result is a phenomenal saving of time for medical staff in finding specific information from vast stores of data.
• Medical transcription: NLP is used to recognize speech and subsequently format it properly.
• Report generation: NLP can visualize data in textual form. These models are trained from pre-existing reports and templates.

Data analytics and visualization: Data visualization is what enables EHR to be effective at making data accessible and available. An EHR is essentially a giant graph full of raw data regarding various aspects of a patient's medical state. The role of ML in this case is to interpret that data into accessible form.

Predictive analytics: One of the most integral innovations that has been presented by EHR is the streamlining of the data pipeline for additional transformation.

Since all patient data and reference databases intertwine at various points into a single system, the leveraging of available data for the prediction of possible outcomes based on existing data is not only possible, it is highly efficient.

Predictive analytics can assist the physician in making decisions by providing various options while taking possible courses of actions into consideration.

Models for predictive analytics are trained case-by-case on EHR databases. By accumulating diverse data, common patterns are identified along with disease development aspects and/or a patient's response to varying treatment methods.

Regulatory and privacy compliance:  Healthcare obviously operates with data that is highly sensitive. EHR can be vulnerable to data breaches or loss. But EHR  are bound by governmental regulations on the gathering, processing and storage of personal data such as GDPR or HIPAA to ensure organizations prioritize the security of this data. 

EHR and the implementation of ML has elevated the healthcare industry to new levels. The overview of wider patient data EHR make available results in better outcomes for patients. ML-fueled EHR are providing clinicians with transparency in the framework for data science that provides accurate data and deep insights. The result is organized healthcare that delivers treatments that heal patients in a timely manner with less negative outcomes.

About the author: Heidi West is a medical writer for Vohra Wound Physicians, a national wound care physician group. She writes about healthcare and technology in the medical industry.