The amount of research and development being done on machine learning and artificial intelligence in healthcare has gained significant momentum since the pandemic of COVID-19. Amongst the possible applications, medical imaging is one of the most promising ones. Machine learning is the most common form of AI. It finds patterns in a large set of data to improve the process of decision-making. Machine learning applications have algorithms, a collection of instructions to perform a certain set of tasks. These algorithms are designed for learning from data, without human intervention.
When advanced analytics tools are used for decoding complex CT scans, MRIs, and other testing modalities, they can demonstrate their ability to dig out useful details for improved decision making, at times with greater precision than humans.
From faster pneumonia diagnosis to cancer detection, AI and Machine Learning in healthcare have proven to be effective tools, especially in the field of pathology and radiology. Machine learning and artificial intelligence healthcare are also used in rapidly identifying diseases from electrocardiograms. Analyzing volumes of data secured from electronic health records is a promising move to extract clinical details and make the right diagnosis. AI and ML can offer real-time scores to transfer care by predicting:
Studies on proof of concept aim to work on the clinical workflow, including predicting the risk of missing out on a hospital appointment, extracting semantic details from transcripts, summarizing doctor-patient consultation, and recognizing speech in doctor-patient conversation.
Challenges for the Healthcare Industry and Present Day Clinical Setting
Looking into 2021 and beyond, here are some of the major challenges that healthcare has to face.
Information and Service Integration
Big data advancement is being welcomed by the medical community. However, the implementation isn’t fluid. Non-relational databases combine patient details from various sources, offering actionable metrics.
Usually, care providers use relational databases for storing and accessing patient information. But, relational databases are unable to manage unstructured data. Non-relational databases exploit the recorded patient details, regardless of the format.
Some care providers have adopted electronic health records, only recently. They are not ready to implement non-relational database technology. With added investment, care providers can augment database effectiveness.
Due to limited access to patient details, drug manufacturers tend to waste loads of money on research and development. This cost is then passed on to the consumers.
Invoice and Payment
According to medical organizations, their main struggle with revenue cycle management is patients are becoming less careful in regards to the medical bills. To help encourage patients to submit payments on time, providers have to abide by patient payment preferences.
For catering to the requirements of the patients and improving user experience, billing statements have to be paid patient-friendly. It is necessary to offer paperless statements and different payment options.
Nevertheless, it is often costly and challenging for medical practices to set up payment and invoicing processing systems in-house. Also, the problem of submitting payments because of a lack of options is another reason for the consumers not paying their financial dues. This is mainly due to a lack of price transparency.
The medical insurance field has gone through some significant changes in recent times. With more patients becoming responsible for their healthcare bills, they are looking for better services from the providers.
Healthcare organizations are facing tougher competition to attract and retain patients who are demanding an experience that matches the customer service provided by consumer brands.
So, healthcare organizations are looking for a streamlined patient experience to resolve most concerns and issues as it is more convenient for them.
Healthcare organizations that are providing different services in different locations should make sure that every employee has access to updated patient details from a centralized location.
COVID-19 pandemic has encouraged minimal in-person human interaction. So, the adoption of telehealth has grown from 11% in 2019 to 46% in 2020. The future implications of COVID are still uncertain but it is, highly likely that telehealth adoption will only increase. 76% of the consumers say they are moderately or highly likely to use telehealth in time to come.
But telehealth still faces major problems like the prospective digital health bubble.
How can Machine Learning Help in Overcoming the Challenges?
Machine learning and artificial intelligence in healthcare will bring significant changes. Acumen Research and Consulting predicts that the global market is going to hit $8 billion by 2026. Technical giants have industry-specific solutions.
Let’s take a look at some of the machine learning medical applications that will help in overcoming the challenges.
Healthcare’s modern approach is to prevent disease early on. Previously doctors used risk calculators for assessing the possibility of the disease developing. The calculator uses basic details, such as medical conditions, demographics, and life routines. The challenge is the low accuracy rate. But with machine learning and artificial intelligence in healthcare , it is possible to get accurate results for disease prediction. Experts are working to fine-tune machine learning algorithms that will be able to provide accurate predictions.
Drug research and development is a costly and time-consuming process. Usually, it takes up to 10 years to develop a new drug and distribute it in the market, and according to the research done by the Tufts Center for the Study of Drug Development, this costs about $2.6 billion
A drug discovery initiative aims to find a compound that will react with the body’s targeted molecule causing any disease to cure. However, there is a possibility that the drug compounds react with the non-targeted molecule of the body negatively and could lead to life-threatening side effects.
Since pharmaceutical companies find it challenging to predict the effect, the chances of a drug failing are much higher. A machine learning-based approach to identify a toxic compound that can lead to side effects can help save many resources before it goes into clinical trials.
Electronic Health Record
Electronic health records have the whole health and medical data in one system to ascertain data accessibility and availability. The systems have different data sources and data that come in various forms, unstructured and structured. Storing this data isn’t a problem, but it’s not easy to deploy data for analyzing and predicting because of its inconsistent format.
Machine learning and artificial intelligence healthcare technologies, such as optical character recognition, image processing, and natural language processing, helps in converting data to uniform format from multiple systems and different sources.
What has to be Done to Ensure that Machine Learning Delivers Value in a Clinical Setting?
When designing machine learning and artificial intelligence model for healthcare, it is important to build intuitive tools that can integrate clinical workflows. Solutions that are built without the input of clinicians can result in development of convoluted technologies, which result in causing more harm than good.
But there are many algorithms in the market that have been developed in a vacuum. The clinical needs have to be matched with the technology in a way that enables solutions to be integrated into the clinical workflow.
If doctors are going to use tools in practice, they have to understand a little about how they work, especially where they might go wrong and where they might fail.
Machine Learning Transforms Outcome and Delivers a Clinical Impact
Machine learning applications can improve the accuracy of treatment protocols and clinical outcomes through algorithm processes.
For instance, deep learning, a kind of complex machine mimicking the human brain functions, is being used in medical imaging and radiology. Using neural networks, which learn from data without supervision can recognize, detect, and analyze cancerous lesions from images.
Machine learning and artificial intelligence in healthcare can detect faster processing speed for detecting anomalies in images beyond what the human eye can detect.
Future advancements in machine learning will keep transforming the clinical field.
ResolveData is Driving an Impact in Clinical Setting
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