Category Archives: Healthcare IT

AI in Healthcare

AI in Healthcare Insurance

AI has transformed industries and has immense potential to reshape the healthcare landscape. Medical data like patient visits, prescriptions contain valuable information which can be extracted to create a more structured data format. This has become possible with Artificial Intelligence and Machine Learning. This in turn offers insights into the trends and facilitates collaboration between different healthcare units. AI applications range from early detection of symptoms to potential diagnosis at a fraction of the cost and increased accuracy.

What is Optical Character Recognition?

Artificial intelligence refers to the use of Machine Learning algorithms to mimic human behavior. Say we want to create a program that takes an input and gives an output. The program uses some decision-making mechanism to arrive at an output. This is primitively done by something called IF-ELSE statements. The limitation of these statements is that one needs to know the exact condition that will produce the desired output. For example, we know the marks of a student and we want to assign a grade, we can hardcode the same using if-else statements – IF marks > 90 then grade = ‘A’. In this case, we have a clearly defined boundary that helps us arrive at our output. But what happens when we don’t have a clearly defined boundary?

Let me explain this with an example. Suppose we have an image of a letter. And we want our program to tell us which letter it is. What kind of code shall we write? This is an example of a problem that doesn’t have a clearly defined boundary. One way to solve this problem is to build a model, which does not use if-else statements but works quite differently. The idea is to feed the program an input image (say an image of ‘A’) and tell the program that it’s an ‘A’. We do this repetitively and try to get the program to learn what ‘A’ looks like. Hence, the term “Machine Learning”. We do the same for every letter or number. This falls under the computer vision category, namely Optical Character Recognition.

Overview of Natural Language Processing :

Natural language Processing is an area of AI and Linguistics and it deals with the application of computational techniques and models to the analysis of human languages. NLP deals with processing written text including but not limited to extracting useful information such as named entities, etc. This requires an understanding of what a word means, which is not something computers generally have. This too is done by learning, where a huge amount of text is fed to the model, and it will try to learn patterns and dependencies between words. In the next section, we will define a problem statement and see how an application of these two concepts can help us make better decisions faster, way faster.

Problems faced by the Healthcare Industry :

Insurance companies have certain parameters to account for when finalizing the premiums they charge for their insurance. One of the most important parameters is the medical history of the member/ insurance holder. The idea is that if a member has more cases of medical history then that member is more likely to raise a claim in the future, thereby costing more.

So the insurance companies need to know relevant details of the medical history and assign risk scores (this could be a topic for another article) corresponding to the medical history. But medical histories are generally stored in paper form. Suppose you had a doctor’s visit, everything there is generally stored in paper format. One might have had any number of tests, and these are in most cases available in paper form. Thus, going through all the paperwork of a member’s medical history is an engaging task. This might take a person anywhere between 30mins – 1 hour. So, is there a way to automate the task?

How can AI help to resolve the issue?

The first step in the process is to get the images of all our documents. The idea is to use OCR to extract the text information from images and subsequently use NLP to extract relevant information, such as diagnosis, procedure or operation, medications that the member is on, etc. There are many open-source OCR engines, one such engine is Tesseract OCR.

Tesseract is used for text detection within large documents or used in concurrence with an external text detector. It can be used directly or through an API to extract text from images. It supports Unicode and has the ability to discern more than 100 languages and can also be trained to recognize multiple languages. The current model, Tesseract 4 focuses on line recognition. It also supports the legacy Tesseract 3 in recognizing character patterns. It is compatible with many programming languages and frameworks. CNN (Convolutional Neural network) is typically used for single character whereas RNN is used for multi-characters.

The next step is to feed the image to the tesseract engine. It will give a file output that contains text extracted from the image. There are various Open source NLP engines. We then feed lines from the text file to our NLP engine which will read each line and use dependencies between words to figure out what our diagnosis, procedure or medications will be. Most NLP engines offer phrase matching capabilities, so we can look for particular phrases as well. We can narrow our search to a specific diagnosis (or any other information of relevance) as well.

Final Steps :

Once we have extracted output from the NLP engine, we can create a database that will have fields such as member name, document type, extracted information, etc. We can use the above pipeline and feed it numerous files at once and after every output, we can append the same to our database.

This process takes a mere few seconds to go through all the files of a member. Using this, we will be able to read through the medical history of thousands of members in a matter of hours.

Like other businesses, if you too are looking for Application development in AI, Mindfire Solutions can be your partner of choice. We have deep expertise in AI and ML Capabilities. With a team of highly skilled and certified software professionals, that have developed many custom solutions for our global clients over the years.

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NLP Image

Impact of NLP on Healthcare Industry

Natural language processing (NLP), along with machine learning, deep learning, computer vision, and image recognition, are all branches of artificial intelligence (AI). The goal of NLP software is to build computer systems that will accept input in the form of spoken or written language and will provide spoken or written output i.e. communicate as if the computer system were a human.

Thanks to devices and applications like Alexa, Siri, Google Assistant and Cortana, much of the world’s population has at least a passing familiarity with NLP. It is being used today to perform a wide range of tasks across many industries. Until recently though, healthcare organizations have lagged behind others in capturing the benefits NLP delivers. However, it’s beginning to catch up.

Here are several use cases for NLP in healthcare that are already enhancing the field. Each of these will contribute to the larger digital transformation of healthcare as technology continues to advance.

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Medical Coding and Billing

NLP streamlines the way medical coders extract diagnostic, procedural and other clinical information. Rather than a coder reading documents and converting them to alphanumeric codes, NLP reads them and submits the codes to the coder for verification. This allows the human coder to work on documents that NLP cannot process accurately, and reduces the overall expense of coding medical information. In the end, more accurate and thorough coding results in more accurate and timely billing.

Virtual Nursing Assistants

The rise of virtual nursing assistants capable of communicating with patients using NLP is underway. Regular communication between patients and the nursing bot extends care beyond the walls of the clinic room without burdening existing resources. Adherence to the patient’s care plan can be monitored, and triggers can notify providers of issues that need human attention. Patients can receive round-the-clock access to support and answers, including help with medication. Researchers in this field estimate virtual nursing assistants will reduce U.S. healthcare costs $20 billion by 2026.

Robot-Assisted Surgery

Some surgical robots use AI to apply information obtained from prior surgeries to the current case, leading to progressively better outcomes. Beyond the many well-known advantages, robotic surgery delivers, adding an NLP component allows surgeons to query the system and to direct its actions verbally.

Reducing “EHR Burnout”

Recent studies have indicated that healthcare providers spend nearly half of each day updating electronic health records and doing other administrative work, which is a matter of concern. It leaves them with very less time to perform their core functions of examining and discussing clinical, diagnostic and treatment information with patients in a face-to-face environment.

Entering and managing patient information is a major contributor to physician burnout. More than half of physicians surveyed in a Physician’s Foundation 2018 study, reported entering data into the EHR reduces their efficiency and detracts them from their interaction with patients. Systems that use NLP allow physicians to enter notes into the EHR by speaking to it. This saves time versus having to type. Besides, it also allows patients to amend or correct what the doctor is entering into the EHR.

Other Important Use Cases

While improving the clinical value of EHRs and reducing physician burnout is one of the most pressing challenges among healthcare organizations, NLP is contributing to the digital transformation of healthcare in several other ways. For example, NLP is helpful in

While improving the clinical value of EHRs and reducing physician burnout is one of the most pressing challenges among healthcare organizations, NLP is contributing to the digital transformation of healthcare in several other ways. For example, NLP is helpful in

  • Comforting patients who become confused and anxious because they do not understand the data being presented to them through a portal website. For instance, NLP can explain the meaning of abbreviations and medical terminology. Rather than leaving the patient to worry or call the physician to explain the report, NLP can educate and possibly also calm the patient.
  • Offering summarized updates of key ideas, concepts, and conclusions contained in large volumes of clinical notes, journal articles and other narrative texts gives practitioners quick access to volumes of information that would otherwise require a lot of time to read through.
  • Easy extraction of data from free-form text and insertion into fixed-field data files, such as the structured fields in an EHR.
  • Handling a physician’s free-form spoken or text query, which is especially useful for queries that require gathering and organizing data from multiple sources.
  • NLP and other AI components can also accelerate the movement away from fee-for-service models and toward value-based healthcare by organizing unstructured health data derived from EHRs and other sources. Much of that “hidden” big data can shed light on health outcomes for entire populations of patients, which has been impractical until recently.

 

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If you have any queries in this field, talk to Mindfire Solutions. For over 20+ years now, we have been the preferred Software Development Partner of over 1000+ Small and Medium-sized enterprises across the globe.

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Healthcare IT Trends in 2018

Healthcare IT Trends in 2018In 2017, worldwide healthcare IT market grew steadily despite cost pressure and political uncertainty. Many enterprises leveraged latest digital technologies to provide remote and mobile healthcare services in an efficient and timely way. At the same time, the new healthcare applications and solutions introduced by various startups even made it easier for medical practitioners to provide mhealth and telehealth services to patients regardless of their current geographic locations. But the rapid advancement in digital technologies has made some of the hottest healthcare IT trends of 2017 obsolete. The healthcare service providers must adopt new healthcare IT trends in 2018 to stay relevant and competitive in the short run. Continue reading Healthcare IT Trends in 2018

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