Month: March 2021

In healthcare, sharing information is vital to the security and safety of the sector, and stakeholders within the sector.

The continued push for nationwide interoperability has helped fuel the growth of secure healthcare data sharing. Covered entities and business associates are exploring how to enhance patient care by engaging in health information exchange (HIE), but are also concerned with how they keep that data secure. Sharing patient information can help providers reduce readmissions, avoid medication errors, and even decrease duplicate testing.

What are the benefits of sharing data?

Sharing data is essential if we are to provide the very best care we can to patients. It also enables our stretched healthcare services to work in the most efficient way possible. Reducing pressure on urgent care services is a good example of the very tangible benefits of data sharing. The benefit of having access to tens of thousands of data sets at one time. Sharing information matters because we all need to be aware of what is going on and understand the consequences of what may occur.

Good information sharing is a good privacy and security practice which helps protect our organizations and our patients. Improving patient outcomes and reducing cost isn’t just about data, it’s about utilizing data effectively and turning it into a usable source.

• Healthy patients: Monitoring applications of vital signs to ensure a proactive approach to a person’s healthy state is monumental. For example, diabetes patients can track their insulin dosages, next medical appointments, and more.
• Cost reduction: data sharing offers the ability to manage information and use it to drive cost improvements. Healthcare organizations can identify areas where cost reductions can be made, whether related to admission rates, diagnostic tests, or operational procedures.
• Error minimization and precise treatments: Data sharing in healthcare enables providers to deliver more accurate and personalized care treatment. By having a detailed picture of patients, it is easier to predict the response to a specific treatment.
• Prevention services: Preventive care to provide services more efficiently, optimize operations, and improve the prevention of medical risks.
• Streamline hospital operations: with data being generated at breakneck speed, hospitals have the demanding role of managing the operational aspects of the facility.

Learn more about data sharing in healthcare:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284141/

https://academic.oup.com/eurpub/article/29/Supplement_3/23/5628051

https://www.nap.edu/read/21703/chapter/4#37

Artificial intelligence and predictive analytics continue to make transformational changes throughout the business world. While healthcare IT is seeing some impacts from AI and machine learning, it lags behind other industry sectors in fully adopting this emerging technology. However, necessity is sometimes the mother of invention, as evidenced by the Coronavirus pandemic still ravaging the planet.

Because of the strain and unpredictability COVID-19 is causing hospitals and medical establishments, faster adoption of AI is now critical. Unfortunately, healthcare IT departments suffer from a skills gap as well as other issues when implementing new systems using machine learning. Nonetheless, the demand for AI remains strong.

Here are some of the major 2021 trends related to AI in the healthcare industry:

AI Making Inroads in Healthcare over the Last Year

 The healthcare industry has seen some impacts from AI over the last year or two. For example, at-risk populations benefit from the AI-powered monitoring of chronic issues. This area especially holds promise for the treatment of COVID-19 patients; illustrating how machine learning models have the potential to augment the decision-making processes of medical professionals. Additionally, this proactive monitoring approach prevents costly emergency hospital trips.

As noted earlier, COVID-19 is driving the increased adoption of AI and ML in healthcare. One benefit is improved decision-making regarding patients, both in diagnosing the virus and in treatment. Additionally, predictive analytics helps healthcare organizations improve operational efficiency to better handle a physician shortage exacerbated by the pandemic. AI also helps doctors better diagnose diseases by using image analysis powered by machine learning algorithms. It’s another example of how ML enhances the ability of the medical professional. This image interpretation use-case applies to X-Rays, angiograms, CT, and MRI scans.

Improving Efficiency in Hospital Operations

In addition to helping hospitals streamline their operations, AI provides notable benefits in the area of revenue cycle management.  AI reduces the task of manually reviewing thousands of medical claims, and instead focuses our staff on performing warm, human outreach, and thinking through complex problems together with our members. Machine learning algorithms help hospitals perform information retrieval more effectively. These ML-powered software search agents automatically ensure patient or other data is up to date. As the use of EMR for patient data becomes more commonplace, expect AI to play a significant role in ensuring efficient data management.

The Benefits of an Open-Source AI-Layer for Databases

Data for many healthcare organizations remains difficult to access; requiring the skills of a data scientist to wean actionable information. In this scenario, an easy-to-use AI-layer able to connect to and query a database offers significant benefits. Users execute ML models with a simple SQL query. They receive relevant results along with an explanation of how the model derived its result set.

Making the power of AI/ML-driven solutions easily accessible to medical and insurance professionals improves the adoption of these nascent technologies. The ultimate result is increased efficiencies and improved benefits for the healthcare industry.

Learn more about AI trends in healthcare:

https://www.sciencedirect.com/book/9780128184387/artificial-intelligence-in-healthcare

https://www.sciencedirect.com/science/article/pii/B9780128184387000022

https://www.sciencedirect.com/science/article/pii/B978012818438700006X

Interoperability is the ability of different information systems, devices and applications to access, exchange, integrate and cooperatively use data in a coordinated manner. 

Why is interoperability important in healthcare? 

Interoperability in healthcare leads to quality patient care, as data can flow seamlessly across the continuum of care and be accessed by relevant parties when needed.   

Benefits of healthcare interoperability  

Here are six ways interoperability improves healthcare delivery and patient experience through better information data exchange.  

Improves clinician efficiency 

With a comprehensive view of each individual patient, pulled from both traditional and non-traditional sources, health providers can make a better assessment of the patient and deliver better care.  

Enables safer transitions of care 

Patients with chronic conditions or those who are transitioning from hospital to community care require continuity of care for optimal health results. Interoperability enables safe transitions by allowing multiple health providers to access relevant patient information in a timely manner. 

For instance, a patient discharged from hospital visits the GP for an illness. The GP can see a full medical history of that patient, their recent procedures and medications prescriptions from the hospital and can then organize treatments accordingly. 

Can save time 

if a patient changes their healthcare provider, interoperability can help save time. A patient may forget details of their treatment, types of medication and relevant lab results. With interoperability, this information is easily transferred between providers and viewed by the current healthcare provider.  

Helps lower healthcare costs 

A patient who has had some lab tests recently done under their regular GP’s direction does not need to retake a blood test if they decide to visit another clinic shortly after that. The GP can view the recent test results and make clinical decisions, saving time and cost of doing more unnecessary lab tests.  

Maintains patient privacy 

Patient privacy is critical in the health sector. The right to doctor-patient privacy can benefit tremendously with interoperability. Cutting the need for clinical staff and manual updating of patient records means patient privacy can be maintained effectively. 

Reduction in errors 

Data accuracy in healthcare is crucial to avoid treatment or medication errors, as in some instance’s medication errors can occasionally pose serious health threats.  Records that are successfully exchanged across systems, typically following international standards, are guaranteed to meet data quality, thereby limiting the chance of such risks. 

Learn more about the benefits of Interoperability in healthcare:

https://ehrintelligence.com/news/3-ways-to-enhance-healthcare-interoperability-with-health-it

https://www.forbes.com/sites/forbestechcouncil/2020/05/11/how-interoperability-can-improve-patients-healthcare-process/?sh=3a82df743580

NLP, a branch of AI, aims at primarily reducing the distance between the capabilities of a human and a machine. As it beginning to get more and more traction in the healthcare space, providers are focusing on developing solutions that can understand, analyze, and generate languages can humans can understand.

Natural language processing (NLP) is the ability for computers to understand the latest human speech terms and text. It’s used in current technology to support spam email privacy, personal voice assistants and language translation applications. 

The adoption of natural language processing in healthcare is rising because of its recognized potential to search, analyze and interpret mammoth amounts of patient datasets. Using advanced medical algorithms, machine learning in healthcare and NLP technology services have the potential to harness relevant insights and concepts from data that was previously considered buried in text form. NLP in healthcare media can accurately give voice to the unstructured data of the healthcare universe, giving incredible insight into understanding quality, improving methods, and better results for patients.

Big data analytics in healthcare shows that up to 80 percent of healthcare documentation is unstructured, and therefore goes largely unutilized, since mining and extraction of this data is challenging and resource intensive. Without NLP technology, that data is not in a usable format for modern computer-based algorithms to extract. 

How Healthcare natural language processing work?

It uses specialized engines capable of scrubbing large sets of unstructured health data to discover previously missed or improperly coded patient conditions. Natural language processing medical records using machine-learned algorithms can uncover disease that may not have been previously coded, a key feature for making HCC disease discoveries.

EHRs and physicians don’t always get along well. The additional data input responsibilities create challenges, and can be frustrating. Researchers conclude, some physicians suffer from EHR burnout and threaten to retire from service early rather than suffer through the many clicks and screens required to navigate their EHR. Medical NLP is steadily proving to be a solution to this challenge since NLP healthcare tools can easily access and accurately interpret clinical documentation. Once the friction of healthcare technology is reduced, we can begin to appreciate more of the benefits of the technology and less of the daily frustrations. 

The accuracy of medical natural language processing

It goes up along with the volume of data available for learning. The more a medical NLP platform is used, the more accurate using artificial intelligence in healthcare gets, since it’s always learning, and in some cases, can be customizable. Some NLP healthcare systems offered by vendors advertise the ability to screen how the medical natural language processing would initially perform with a specific medical group. Then customize it next to the needs of that particular medical group. 

A distinct advantage natural language processing medical records offers is the ability for computer assisted coding to synthesize the content of long chart notes into just the important points. This could take organizations weeks, months, even years, to manually review and process stacks of chart notes from health records, just to identify the pertinent info. Natural language processing software for healthcare can scan clinical text within seconds and identify what needs to be extracted. This frees up physicians and staff resources to focus more on the complex matters and reduces the time spent on redundant administrative policy. When computers can understand physician notation accurately and process that data accordingly, valuable decision support can be obtained. These insights can be of significant use for future drug research and personalized medicine, which is good for patients and providers.   

Physicians don’t all “speak the same way”, and should always be aware that their notes and reports will likely be read by their work peers, patients and even computers, according to their organizations privacy policy. Avoiding non-standard language in note creation and management is extremely important. Most natural language processing healthcare engines are built to accommodate a wide variation of medical notation terminology. However, using uncommon acronyms can confuse NLP coding algorithms and other medical note readers.

NLP help in identifying Patients who need improved Care

Machine Learning and NLP tools have the capabilities needed to detect patients with complex health conditions who have a history of mental health or substance abuse and need improved care. Factors such as food insecurity and housing instability can deter the treatment protocols, thereby compelling these patients to incur more cost in their lifetime.

The data of a patient’s social status and demography is often hard to locate than their clinical information since it is usually in an unstructured format. NLP can help solve this problem. NLP can also be used to improve care coordination with patients who have behavioral health conditions. Both, Natural Language Processing & Machine Learning can be utilized to mine patient data and detect those that are at risk of falling through any gaps in the healthcare system.

Future of Healthcare natural language processing

In 2018 and 2019 the development to improve natural language processing healthcare data has proven challenging. If the NLP output displays too many suggested conclusions, or artificial conclusions that are incorrect, users will learn to ignore the intelligence and end up with a system that can reduce overall business productivity. NLP software for healthcare should center around data conclusions that have the least noise, and the strongest signal about what healthcare providers need to do. 

Healthcare natural language processing offers the chance for computers to do the things that computers need to do. To do the analytics, the HCC risk adjustment coding, the back office functions, and the patient set analysis, all without obstructing physician communication. 

NLP in healthcare is creating new and exciting opportunities for healthcare delivery and patient experience. It won’t be long before specialized NLP coding recognition enables physicians to spend more time with patients, while helping make insightful conclusions based on precise data. In the years to come, we’ll hear the news, and see the possibilities of this technology, as it empowers providers to positively influence health outcomes.

Learn more about NLP in healthcare:

https://www.sciencedirect.com/science/article/pii/S1532046418302016

https://healthtechmagazine.net/article/2020/05/language-processing-tools-improve-care-delivery-providers

What Is Big Data?

“Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.”

The insights gained from big data can allow businesses to solve problems that could not be tackled with traditional software or analytics. In healthcare, these new insights can help researchers gain a deeper understanding of data in order to improve the results of clinical trials, boost the productivity of healthcare professionals and improve revenues of the practices themselves.

Big data relies on three Vs: 

• Volume 
• Velocity
• Variety

Volume

This is the amount of data generated, such as through mobile apps, websites, portals and online applications.

Velocity

Velocity is how fast data is generated. 

Variety

This is the generation of both ‘structured data’ and ‘unstructured data’. Variety is about being able to translate data into specific categories.

How Healthcare Uses Big Data

Product Development

Discovering and developing new drugs and other health-related products takes an amazing amount of time and money.

Big data can help reduce the time involved in a number of different ways. This, of course, serves to reduce the costs involved.

Product development involves lengthy trial and error experimenting, which requires much time. Big data removes the guesswork allowing research and development companies to get results more quickly and thus develop more precise products faster.

Patient Outcomes

Big data improves patient outcomes because it helps doctors and other medical professionals be more efficient and accurate with their diagnoses and treatments. With the improved data analyzing methods big data provides, doctors can hope to find solutions to treat rare and serious conditions that would otherwise seem incurable because research can progress at a faster pace. 

Driving innovation

This is perhaps one of the largest uses of big data in healthcare. Without innovation, there would be no advancements in medicine at all. Big data will speed the rate at which new drugs can be discovered and the quality of care is improved.

The goal of efficiently using big data is to understand what is going on, identifying problems, and finding innovative solutions to them that will help reduce costs.

Who Benefits From Big Data Analytics?

Providers (Clinics, Hospitals)

The insights generated from big data analytics enables healthcare providers, such as clinics and hospitals, to improve patient care.

Patients

Patients will benefit from big data in healthcare more than anyone else. They can enjoy better overall care, live healthier lives, save money on insurance and so much more. 

Manufacturers

Big data analytics will enable device manufacturers to create better, innovative products to solve health issues. They will benefit from devices relevant to their needs.

Pharma

A 2013 study, published by Nature Review Drug Discovery, found that only 10% of medicines in development ever reach patients. With this in mind, big data in pharma will benefit from better research and development, resulting in more effective drugs and shorter production times. Pharma companies will also save on the costs related to drug development.

References:

• Big data in healthcare: management, analysis and future prospects | Journal of Big Data | Full Text (springeropen.com)
• Big data analytics in health informatics | International Journal of Development Research (IJDR) (journalijdr.com)
• Big Data, Big Problems: A Healthcare Perspective – PubMed (nih.gov)