Enhancing the provider EHR has always been a top priority for all healthcare organizations across the world. The industry, however, can be seen to be getting tossed between giving its patients undivided attention while having to complete the herculean task of documentation.

This leaves them feeling worn out and discontented. According to some findings in the New England Journal of Medicine (NEJM), physician burnout is a major problem in the industry as identified by healthcare executives, clinical leaders as well as the clinicians. So what is it that we can do to minimize this?

Innovation doesn’t necessarily mean coming up with new technologies; it is all about focusing on how to put new technologies into the best possible application to improve the existing processes. It will make sure that the providers can come together to ensure better care to the patients, thereby also enhancing financial and clinical outcomes.

Many of the trials and tribulations of the healthcare industry can be addressed by bringing Natural Language Processing (NLP) into the picture, as it can provide aid and assistance in enhancing the delivery of care. The combinations of underlying algorithms have already proven useful as it simplifies clinical documentation and enables voice-to-text dictation.

What is NLP?

TechTarget defines Natural Language Processing as an integrant of machine learning which is the ability of a computer program to comprehend the human language by grasping the context, intent and some entities present in a particular sentence. Its focus is on making computers understand and analyze a substantial amount of natural language data.

This would mean that the users can raise queries to data sets as if they are posing the questions to another person. What the machine does is that it takes in the intent with the help of some entities from the sentence which correspond to certain features in a data set, thereby returning the answer.

Improving Provider Interactions with Patients and EHR

The additional cost burden of getting the documentation done while not compromising on giving the patients adequate attention has been a tremendous challenge for the clinicians, thereby making it a healthcare epidemic. One feasible solution to this EHR distress is offered by NLP tools such as voice recognition. Thanks to this, providers are resorting to voice-based dictation tools to minimize their frustration.

An illustration of a voice-to-text application of NLP in healthcare

For primary care providers, it ensures a much more efficient and cooperative approach, as it enables them to talk to the patient as well as the record at the same time. This lets them spend more time in the patient’s presence, without having to recount the visit again at a later point of time. Many healthcare organizations have already incorporated virtual assistants like Siri, Cortana and Alexa to assist them with tasks related to help desks and customer service. Over the due course of time, the healthcare industry may also be able to witness these tools donning the roles of medical scribes.

Apart from rendering support to the healthcare domain in terms of documentation, NLP offers other advantages as well.

Enhancing Patient Health Literacy

More and more healthcare organizations have started using patient portals that provide the patients access to their health data, aid them in making more informed choices and to keep their health on track. But this doesn’t mean that the patients are able to completely follow the data given in these portals. Some reports suggest that even though patients could access their data, some found it difficult to comprehend the given information, while only a few made use of these data to make medically informed choices.

Minimal patient literacy rate can also weigh on the providers. Providers often have to put in extra time and effort to help the patients understand what some terms mean. In some cases, they also need to pacify other patients who might have misread any lab tests or diagnoses.

NLP tools may be the answer to the smooth interaction between the healthcare IT tools and the patients. The application of NLP to the EHR data, followed by the integration of the same to the patient portal, would ensure a better understanding of health-related data for the patients.

Some experiments have already been conducted using NLP tools to link some medical terms to their equivalents in layman’s terms. During these experiments, a high precision was observed in the performance of the NLP algorithms. This highlights the potential of these algorithms to promote a better understanding of the EHR in the future.

Ensuring a higher quality of care

Evaluating and improving the quality of care is yet another advantage of using NLP tools. Assessing physician performance and discerning the gaps present in the delivery of care can be considered a core competency for organizations that are trying to switch to value-based reimbursement.

Creating a benchmark for the professional skills of the physicians, automation of evaluating free text as well as the minimization of the time and effort invested in completing this task – all of this can be achieved by the application of NLP. This is evident from some studies wherein researchers applied NLP to a set of free-text questions that were filled out by the peers of the providers. In these studies, the NLP algorithms were found to agree with the assessments by humans almost 98 percent of the time.

Providers can also rely on NLP for the identification of potential errors in the delivery of care. One example of this can be seen in the research wherein an NLP tool was applied to unstructured data to spot adverse drug events in social media postings and medical literature. This resulted in the development of many drug risk-benefit evaluation schemes to probe into drug effectiveness and safety through various medical data sources. The NLP tool was observed to be more accurate and precise as compared to the traditional big data analytics tools. This helps in demonstrating NLP’s capability of enhancing care and ensuring the safety of the patient.

Moreover, NLP can also aid providers in monitoring compliance with clinical guidelines and in gauging the quality of inpatient care. For the extraction of the EHR data, researchers have come up with an NLP system that identifies whether relevant medicines were prescribed to the patients when they were discharged from the hospitals.

Identifying Patients who require enhanced care coordination

NLP tools can also be used to detect complex patients who could benefit from enhanced care. Due to many non-clinical factors like food insecurity and housing instability, it can prove difficult for patients to stick to the treatment procedures, leaving them to incur more care costs during their lifetimes.

example of NLP-assisted system identifying 'at risk' patients

The consolidated data that elaborates on the determining social factors of the health of these patients is more inaccessible and unorganized than its clinical counterparts. Through the use of NLP algorithms, the extraction of information from large datasets can prove to be useful for the clinicians for the detection of such complex patients. This can be done by applying NLP techniques to EHR to help providers identify the key terms in association with the social determinants of health.

For patients with behavioral health issues, NLP can again prove to be beneficial. They can help in mining unstructured patient data thereby recognizing those who are prone to fall through the gaps in the healthcare system.

While it is true that a lot of refinement is required in the healthcare sector regarding its data capabilities before the NLP tools can be deployed within the clinical organizations, it can be said with surety that NLP offers a brighter future for the healthcare domain by lending support in clinical decisions, enhancing the delivery of care as well as in rendering better patient health outcomes.

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