How AI Search Can Change Healthcare
Though data management presents a challenge to most industries, technological advancements have been able to mitigate some of these challenges. Healthcare is also one of the industries that constantly adopts new ways to solve challenges. Trial and error in the clinical sphere proved that data accuracy, accessibility, storage, and retention must be approached with the utmost care. As part of healthcare ever-evolving data management challenges, technology has helped make information an easier aspect of the medical staff’s responsibilities.
Implementing technology on the data end of the process was difficult. Becoming acclimated to digitized records and information sources, when the industry functioned differently a few decades ago, was a necessary and facilitating change. Healthcare staff learned to ensure that their digital patient records were accurate, that they were retained for the period required by law, and that they are securely stored and easily accessible.
We’ve always pinned our hopes -especially in regards to healthcare- on technological advancements and specific medical solutions. And even though we have created tools for advanced diagnosis, complex surgeries and rehabilitation, these technological advances are very purpose-specific, created to treat a certain issue. Data management, however, affects patients with almost any condition at any severity.
Every department and every function relies on medical data being made readily available for use in a time-sensitive and practical way. A large portion of clinical operations can be time-critical and navigating large databases can become a strenuous, increasingly difficult task over time.
Data can be the difference between choosing one treatment over another, accurately diagnosing a patient, and even determining the correlation between unlikely symptoms and an unlikely diagnosis. While that is all very well and good, how exactly do we make data more accessible to medical staff? After spending nearly a decade designing and writing various medical software systems, this is a topic that is always interesting to me.
Data Management in the Age of AI
Cognitive Search is a new generation of data management technology that utilizes AI to offer users in-depth information. It greatly improves user search by analyzing, understanding, and incorporating complex data from several data sources.
While diagnosing a patient, medical staff can utilize cognitive search technology to analyze the patient’s history and medical records, analyze symptoms and past treatment, as well as retrieve information from external sources. The AI search technology then analyzes all the data for the most relevant and accurate results based on those factors.
The technology is a credit to healthcare operations. It provides access to relevant information that can help medical staff prepare for patients and retrieve diagnoses, procedures, and patient records as needed.
Next-generation of search solutions
The majority of commonly used search tools are keyword-based. They run the term you enter into the search bar through your database and display results that contain it. For large databases, such as ones used in the medical space, this could mean that vital information might just be buried under a mountain of irrelevant search results, or more commonly, stored on external databases that are not being searched.
There are search solutions that go beyond that approach. One notable solution is Mindbreeze, an AI-powered cognitive search platform. Mindbreeze Inspire’s solution for the medical industry is known as the Medical Cockpit and is used in a number of hospitals in German-speaking countries.
It is a tool designed to gather all information about a specific treatment or a patient’s records, and intelligently extract diagnoses, symptoms and medication information from documents and present them an in-depth chronological display. The Medical Cockpit understands medical terminology and syntax and can reliably identify specific patterns in findings, diseases, and medication.
Through AI machine learning, Mindbreeze’s medical cockpit provides in-depth insights from large and at times disjointed data sources. Both the interface and search sorting are customizable to assist with patient treatment and provide practical filters for specific technical and medical aspects.
The Medical Cockpit collects all relevant diagnoses, prescriptions, and symptoms, which are found in all patients' documents. A timeline gives a quick overview of patients visits.
Care-specific profiles pre-select the information that is associated with the specialty and type of treatment. For example, all episodes of intensive care and operational procedures are preselected in a surgeon-profile, hiding documents of internal medicine, which may be reselected by choosing the general profile.
Special search widgets can extract unstructured information about allergies, implants, pacemaker and so on. With the Medical Cockpit, information from all types of structured and unstructured medical documents — such as discharge letters, test results, and findings, laboratory and reports, can be specifically filtered and processed for medical personnel. The system provides medical staff with a quick, subject-specific overview of the current patient based on their individual access rights, and extracts diagnoses, symptoms, and medications from the many different documents.
How does Mindbreeze InSpire Work?
Mindbreeze InSpire changes how users access data in their day to day work activities. It is capable of shifting through internal and external databases, and sort results at a higher level of relevance and accuracy. The tool takes more than just the entered term into account but goes beyond that to provide a customizable interface that displays results based on time period, purpose and context.
Mindbreeze InSpireSemantic data processing is handled in a 4 tier architecture. Where each stage can be configured from the binary content level to the structured information and items and their relation to the content.
Mindbreeze combines different methods and mechanisms such as text classification, entity recognition, predictive models, machine/deep learning, and artificial intelligence for meaning-based computing, feature selection, and reasoning. This multi-stage semantic processing is not only used for document, metadata, and content processing, it also applies to the query processing.
Typically, several processors are combined into a content analysis pipeline. There are two important aspects of content enrichment and query expansion. Where query expansion can provide real-time feedback to changes in the schemas and vocabularies.
Mindbreeze InSpireSemantic data processing in a 4 tier architecture
Mindbreeze graph database
Mindbreeze InSpire graph database allows for recording, updating, and maintaining metadata attached to indexed items. The graph database not only stores items and metadata, but it also creates updatable references between these items and metadata.
When a change to metadata must be made, this change does not have to be carried out in each individual document to which this change applies. Instead, the metadata item is updated in the graph database and this change will be propagated to all relevant documents via their references.
Secondly, by using these references, relationships can be inferred and understood, and information insight can be gathered. This gathered information could then be used to enhance the medical staff’s search experience. With the graph database, the longer the system is active and the more references and relationships are created, the more intelligent it becomes.
Although improved data management can be a credit to almost any industry, it is interesting to examine just how Mindbreeze InSpire affects operations in the healthcare sector. While its main benefit allows medical staff to easily access information, the algorithms that Mindbreeze InSpire utilizes, provide aid in making decisions. Decisions such as which treatment to apply for a specific patient after the cognitive search has analyzed medical history, patient allergies, or minor details that a doctor would consider in their diagnosis. It is a tool to both retrieve data and analyze it for case-specific information. For all intents and purposes, Mindbreeze InSpire’s medical cockpit is its own second opinion.
The goal is to make relevant information easily accessible and minimize the amount of time spent on retrieving relevant data, allowing doctors to focus on patient treatment. The Mindbreeze InSpire appliance can perform research through semantic analysis and networked searching. Its functions allow it to scan previous comparable cases and patient records for similar symptoms. This helps medical staff reach an informed, data-driven diagnosis in record times.
Mindbreeze InSpire presents AI-aided data solutions for medical institutions of all sizes. Whether it is case-specific information, a compact overview of a patient’s medical history or practical support for doctors and medical staff in their day-to-day work, it is a tool that has the potential to revolutionize data management for healthcare professionals and is one of the most promising utilization of present-day AI technology. As medical advancements go beyond treating illnesses and diseases. It is not only a question of what we achieve clinically but how practical would administering these advancements to patients become.