Electronic Records in Present-Day Healthcare System
Tags: Healthcare, Machine Learning, Health Technology, Artificial Intelligence, NLP

At present the healthcare system witnesses transmission of patients’ medical records from paper to electronic. After the US adopted the mandated switch to electronic records, they received extensive news coverage both in medical and mainstream publications. The digitalization era has given birth to a number of terms, such as electronic health records (EHRs) and electronic medical records (EMRs) that stand in the foreground of such publications, used sometimes interchangeable. However, there are distinct differences between them — as well as between other newly coined terms that describe different approaches to digitalization of the medical life.

Electronic Health Records?

An electronic health record (EHR) is a digital version of a patient chart, an inclusive snapshot of the patient’s medical history. It contains input from all the practitioners that are involved in the client’s care, offering a comprehensive view of the client’s health and treatment history.

Electronic health records are designed to be shared with other providers and authorized users may instantly access a patient’s EHR from across different healthcare providers.

Elements of EHRs

As a rule, EHRs contain the following data:

Patient’s demographic, billing, and insurance information;

Physical history and physicians’ orders;

Medication allergy information;

Nursing assessments, notes, and graphics of vital signs;

Laboratory and radiology results;

Trending labs, vital signs, results, and activities pages for easy reference

Links to important clinical information and support

Reports for quality and safety personnel

Electronic Medical Records

An electronic medical record (EMR) is a digital version of a patient’s chart used by a single practice: a physician, nurse practitioner, specialist, dentist, surgeon or clinic. In its essence, it is digitalized chart that healthcare facilities previously used to keep track of treatments, medications, changes in condition, etc. These medical documents are private and confidential and are not usually shared outside the medical practice where they originated.

Electronic medical records make it easier to track data over time and to monitor the client’s health more reliably, which leads to better long-term care.

Elements of EMRs:

EMRs usually contain the following information about the client:

Medical history, physicals, notes by providers, and consults from other physicians

Medications and allergies, including immunization history

Alerts to the office and the patients for preventative tests and/or procedures, e.g. lab tests to follow-up colonoscopies

Personal Health Records

An electronic personal health record (PHR) provides an electronic record of the client’s health-related information and is managed by the client. It is a universally accessible and comprehensible tool for managing health information, promoting health maintenance, and assisting with chronic disease management. A PHR may contain information from multiple sources such as physicians, home monitoring devices, wearables, and other data furnished by the client. With PHRs, each client can view and control their medical data in a secure setting and share it with other parties.

However, it is not a legal record unless so defined and is subject to various legal limitations. Besides, though PHRs can provide important insights and give a fuller view of the client’s health and lifestyle, its inaccuracy and lack of structure lead to limited use of it in the clinical and medical studies.

Benefits of Electronic Records Offer

Digital medical records may offer significant advantages both to patients and healthcare providers:

Medical errors are reduced and healthcare is improved thanks to accurate and up-to-date information;

Patient charts are more complete and clear — without the need to decipher illegible scribbles;

Information sharing reduces duplicate testing;

Improved information access makes prescribing medication safer and more reliable;Promoting patient participation can encourage healthier lifestyles;

More complete information improves diagnostics;

Facilitating communication between the practitioner and client;

Enabling secure sharing of client’s medical information among multiple providers;

Increasing administrative efficiency in scheduling, billing, and collections, resulting in lower business-related costs for the organization

So where is AI?

Electronic records are expected to make healthcare more efficient and less costly. However, in reality, under less-than-ideal circumstances, workarounds and errors of different types appear and complaints mount. Improving EHR/EMR design and handling requires mapping complaints to specific EHR/EMR features and design decisions, which is not always a straightforward process. Over the last year, more informatics researchers and software vendors have turned their attention to EHR/EMR systems, and more of them have started to rely on AI to give deeper insights into the design and handling of the electronic records. So far AI is used to assist medical professionals with electronic records flow in the following spheres:

Data extraction from free text

The free structure of clinical notes is notoriously difficult to read and categorize with straightforward algorithms. AI and natural language processing, however, can handle the heterogeneity of unstructured or semistructured data making them a useful part of EHRs. At present, healthcare providers can extract data from faxes at OneMedical, or by using Athena Health’s EHR. Apart from them, Flatiron Health’s human “abstractors” review provider uses AI to recognize key terms and uncover insights from unstructured documents. Amazon Web Services recently announced a cloud-based service that uses AI to extract and index data from clinical notes.

Data collection from multiple sources

As healthcare costs grow and new methods are tested, home devices such as glucometers or blood pressure cuffs that automatically measure and send the results to the EHR are gaining momentum. Moreover, data streams from the Internet of Things, including home monitors, wearables, and bedside medical devices, can auto-populate notes and provide data for predictive analytics. Some companies have even more advanced devices such as the smart t-shirts of Hexoskin, which can measure several cardiovascular metrics and are being used in clinical studies and at-home disease monitoring. This means, that future EHRs should integrate telehealth technologies. Besides, electronic patient-reported outcomes and personal health records are also being leveraged more and more as providers emphasize the importance of patient-centered care and self disease management; all of these data sources are most useful when they can be integrated into the existing EHR.

Clinical documentation and data entry

EHR documentation is one of the most time-consuming and irritating tasks in the modern care environment. A recent AMA study found that clinicians spend twice as much time over the keyboard as they do talking to their patients. Artificial intelligence with the help of NLP can automatically assemble and repackage the necessary components of clinical documentation to build clinical notes that accurately reflect a patient encounter or diagnosis. Nuance, for example, offers AI-supported tools that integrate with commercial EHRs to support data collection and clinical note composition. Such carefully engineered integration of AI into the note creation process would not only reduce the rummaging through bins of pieces, but could improve the output design, making clinical notes more useful, readable, and cogent and meeting all requirements for clinical documentation.”

Clinical decision support

Decision support, which recommends treatment strategies, used to be generic and rule-based. AI machine-learning solutions are emerging today from vendors including IBM Watson, Change Healthcare, or AllScripts that learn based on new data and enable more personalized care. For instance, Google is developing prediction models from big data to warn clinicians of high-risk conditions such as sepsis and heart failure. Enlitic, and a variety of startups are developing AI-derived image interpretation algorithms. Jvion offers a “clinical success machine” that identifies patients most at risk as well as those most likely to respond to treatment protocols. Each of these systems could be integrated into EHRs to provide decision support.

Interoperability

AI can address the core interoperability issues that have made it so difficult for providers to access and share information with the current generation of health IT tools. The industry is still struggling to overcome the challenges of proprietary standards, data silos, privacy concerns, and the lingering competitive disadvantages of sharing data too freely. With AI algorithms learning from inter-specialty communication specifics and facilitating shared decision making by mining patient input and feedback, the final clinical note will be the optimal product for the user in line with the interdisciplinary care concept.