healthcare data Archives - Binary Fountain

4 Use Cases for Natural Language Processing (NLP) in Healthcare

nlp-in-healthcareHealthcare organizations have more access than ever to data-driven technology that can improve healthcare outcomes and drive business opportunities. It’s not easy for these enterprise systems to harness the trillions of gigabytes of health data and web content, but Natural Language Processing (NLP) in healthcare is a promising part of the solution.

As the digitization of healthcare continues, the industry is also looking to make better use of unstructured data. NLP describes the ways in which artificial intelligence systems gather and analyze unstructured data from human language to extract patterns, uncover meaning and formulate responses.  In other words, NLP attempts to get to the heart of language formation and use that understanding to automate and improve human processes.

Leveraged properly, the technology enables providers to automate administrative workflows, invest more time in patient care and improve patient experience using real-time data.

In this article, we will cover the most beneficial uses of NLP for healthcare companies, including benchmarking patient experience, review management and sentiment analysis, dictation and EMR implications and predictive analytics.

Here are some of the top use cases for NLP technology in healthcare:

1. Patient Experience and Value-Based Care

The unstructured clinical record and the patient feedback that comes after a visit contain insights into the patient experience that aren’t available in the structured record. NLP technology can identify these gaps by pulling key words and phrases from free text that will inform care decisions and benchmark the patient experience across physicians and locations.

This type of data mining in healthcare, made possible by NLP, can help reduce subjectivity in decision-making and help organizations deliver better, more efficient care to patients.

Meanwhile, the shift to value-based reimbursement means healthcare organizations need to measure provider performance and identify gaps in care for reporting to payers and regulators.

The value-based care model incentivizes both providers and payers to demonstrate positive patient outcomes after leaving the clinical setting. Data-rich health systems are now using natural language processing to analyze post-care survey feedback, online reviews, social media posts, and many other sources of unstructured text. These insights are key to identifying positive and negative patient experience factors that, if optimized or improved, will lead to higher CAHPS scores and provider ratings.

Use Case:

French research group developed an NLP-based algorithm that would help monitor, detect and prevent hospital acquired infections. It made sense of unstructured data from clinical notes and patient feedback, and used those insights to identify early signs of infections and notify clinicians.

2. Review Management and Sentiment Analysis

In addition to patient experience improvements, NLP can help healthcare organizations manage online reviews in a highly regulated industry.

Natural Language Processing technology can collect and analyze the thousands of healthcare reviews posted every day on third-party listings, finding protected health information (PHI), profanity or other content relevant to HIPAA compliance. It can also quickly analyze and evaluate human sentiment of unstructured comments, along with the context of how they are being used.

In this case study, learn how Temple University Health System leverages Binary Fountain’s NLP technology to analyze unstructured survey responses with an accuracy rate greater than 90%, turning qualitative data into quantitative business intelligence about patient experience.

Many healthcare systems also use text analytics to monitor the Voice of Consumer in reviews, so physicians understand how patients talk about their care and can better communicate using a shared vocabulary. Similarly, NLP systems can track consumer sentiment about your healthcare brand by pulling insights from positive and negative words or phrases within reviews or social media posts.

Use Case:

A Sant Baba Bhag Singh University study found that using sentiment analysis from social media data helped providers improve treatments by understanding how patients talk about their Type-1 and Type-2 Diabetes treatments, drugs and diet regiments.

3. Dictation and EMR Implications

An average EMR record runs between 50 and 150 MB per million records, and the average clinical note record is 150 times as large. To manage that administrative workflow, many physicians are replacing handwriting or typing with voice notes, which NLP tools can easily interpret and add to EMR systems.

This application of NLP allows physicians to automatically transcribe their conversation with patients, which means they can commit more time to improving the quality of care. But its implications go further.

Many of the clinical notes in EMRs are in unstructured form, but NLP offers a way to effectively, and automatically, interpret clinical notes. It can pull details from diagnostic reports and physicians’ letters, ensuring that all relevant information is uploaded to the patient health profile. For example, NLP systems could extract any notes in a patient’s electronic record that mention prescribed medications and if they were effective.

Use Case:

2018 study used NLP to process radiology reports looking for pulmonary embolism and postoperative venous thromboembolism, and found that unstructured data analysis identified 50% more cases than structured data alone.

4. Root Cause Analysis and Predictive Analytics

Another exciting, but more complex, benefit of NLP is how predictive analytics can solve population health problems.

Applying NLP to vast caches of electronic medical records can help identify subsets of geographic regions, ethnic groups or other population segments that face different types of health disparities. Existing administrative databases can’t analyze socio-cultural impacts on health at such a scale, but NLP could pave the way for further research.

NLP-healthcare-use-caseAn often-cited example of this NLP application for healthcare companies is its use for Kawasaki diseases, where delays in diagnosis can lead to critical complications. In a 2016 study, an NLP-based algorithm identified at-risk patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians.

At the same time, NLP can be used to analyze unstructured feedback and find the root cause of patients’ concerns or poor outcomes.

For example, healthcare-specific NLP can recognize phrases like “emailed us a video” as a positive sentiment concerning the topic of “Helping Patients Understand” within the “Provider” category. Monitoring long-term trends of sentiment surrounding topics in these categories, like “patient care,” “non-clinical staff” or “facilities,” can help organizations nail down the origins of negative patient experiences for providers or locations.

When patient experience personnel have a true understanding of a patient’s sentiment, they can conduct the appropriate outreach, perform service recovery and build a deeper relationship between the hospital and the patient.

Use Case:

2018 study used NLP to predict suicide attempts by monitoring social media, showing clear indicators of imminent suicide risk by Twitter users who changed their speech patterns on the platform in specific ways. The system’s prediction rate hit 70%, with just a 10% false positive rate.

NLP Made Specifically for Healthcare

Data-driven health systems looking to optimize patient experience, reduce costs and improve care outcomes should consider the myriad insights hidden in unstructured data. With a wealth of patient feedback available, it is imperative for healthcare providers to begin investing and implementing NLP-powered patient feedback management solutions to secure and ensure patient loyalty.

Natural Language Processing is not a one-size-fits-all solution, so NLP systems in the healthcare industry need to understand the sublanguage used by medical professionals and by patients. Binary Fountain’s NLP-driven technology platform was built specifically for the healthcare industry, and can help your organization get the most from both real-time and historical feedback data.

Book a free demo to learn more.

The Critical Role Health Analytics and Information Plays in Your Business

Today, information is all around us. Even large organizations struggle to analyze their most relevant data in meaningful ways. But where manual processes fail, automation provides a solution.

Data plays a major role in every industry, and healthcare is no exception. Healthcare providers can use this crucial data to benefit both the organization and its patients, but only if it can be analyzed efficiently and in detail.

Here we will discuss the critical role health analytics and information plays in your business.

What is Health Analytics, Exactly?

Healthcare analytics is the process by which raw health data is dissected and reconfigured into meaningful, actionable insights. This data includes information like patient history, company expenditure, brand reputation, patient engagement and more.

When analyzed via automated processes like natural language processing (NLP), details and patterns that may have gone unnoticed become transparent so the organization can properly strategize and adjust its operations accordingly.

We will outline the many benefits of analytics in healthcare below.

Provide Better Care for Patients

Healthcare facilities must treat numerous patients every day, all of whom are unique and require individual attention and care. In the past, nurses and doctors had to largely rely on paperwork to maintain records on each patient, which could contain outdated, inaccurate or misleading information. At the very least, filling out and correcting these forms takes precious time for patients and staff. These lapses in efficiency and accuracy can degrade the quality of care given to a patient.

Health analytics and predictive analysis tools, on the other hand, go a long way toward improving patient experience and health outcomes. Patient records can be stored in a secure online database, where they are updated and analyzed to help doctors make accurate diagnoses, develop individual wellness programs, recognize potential health risks, and provide better overall care to each patient.

Manage and Improve Your Reputation

Your brand reputation is directly tied to your organization’s success. And as it turns out, health analytics plays an important role in reputation management, too.

With automated processes that gather online reviews and survey data into a single location in real time, you can easily track your performance on various scales (day-to-day, monthly, year over year) and across different platforms. Health analytics software can also prepare, schedule, and share reports with different teams based on this survey data so everyone stays on the same page.

For a more detailed view of patient sentiment, NLP uses AI to “read” text and find patterns within the data. By processing the text-based data from online reviews and surveys with NLP, you can quickly identify common concerns that require the most attention. Then, you can act upon these insights to improve your operations and reputation moving forward.

Optimize Your Marketing and Engagement Strategies

While maintaining a stellar reputation is crucial for reeling people in, your organization must also reach out to prospective patients and consistently communicate with current ones. Analyzing patient information can help you develop various outreach and engagement campaigns both online and offline.

Based on patient feedback, you can focus your advertising efforts more on services patients appreciate – and less on the services they view less favorably. Healthcare marketing analytics can also help you track how well or poorly certain campaigns are performing. You might find that different demographics are more or less engaged with certain social media platforms, or that you need to narrow your regional outreach, or that you should adjust your messaging based on the target audience.

Of course, all of your healthcare marketing efforts must abide by HIPAA (Health Insurance Portability and Accountability Act) guidelines so all protected health information (PHI) remains secure.

Save Time and Money with Health Analytics Software

The time- and money-saving aspects of healthcare analytics cannot be overstated. As more and more information is stored online, the need for automated data processing becomes increasingly valuable. By investing in these automated solutions, you free up resources to the benefit of your organization. 

Now, staff members can spend less time filing paperwork and more time improving operations, caring for patients, responding to reviews, developing outreach campaigns, and more. Additionally, health analytics gives those in charge of finances a clearer view of costs, ROI and inefficiencies. These insights can help you trim the fat of your operations and better allocate resources – especially when it comes to marketing your practice.

Maintain a Competitive Edge

As a business, you should always strive to outdo yourself year after year. But you are not your only competitor. If you want to steer more patients toward your organization and away from your competitors, you need to know how you stack up against your peers.

Tools like Binary Fountain’s Deep Analytics platform use artificial intelligence to extract key insights from raw data. One such capability on the platform allows you to benchmark your locations against local and national competitors based on aggregated customer sentiment.

By now, it should be clear just how valuable your data is to your organization’s overall success. Truly understanding that data is even more important. Health analytics gives you the insights necessary to acquire new patients, make your current patients’ lives better, optimize your operations and expenditures, and stay ahead of the competition.

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