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How can artificial intelligence help improve patient treatment and care experiences?
How can artificial intelligence help improve patient treatment and care experiences?
Due to an aging population and the rise of healthcare delivery methods such as telemedicine, the amount of unstructured and structured data generated by healthcare organizations has increased significantly. This article will explore various use cases to show how healthcare organizations can use artificial intelligence, machine learning, and data analytics to leverage the increasing amounts of available data, improve patient treatment and care experiences, and increase operational efficiency.
Use Case 1: Capturing and Analyzing Unstructured Data
Unstructured data for healthcare organizations refers to everything from clinicians’ handwritten prescription forms to patient call centers any content of the log. The amount of this information is increasing, requiring new ways to capture and analyze this data.
In this regard, Tripti Sethi, senior director of the Global Data and Artificial Intelligence Center of Excellence at Avanade, provided an example of work completed using the Answer ALS research project. This example is a healthcare organization looking to leverage big data and artificial intelligence to find answers and treatments, with the goal of leveraging cloud computing, machine learning, large amounts of patient data, and a powerful interactive data infrastructure to help identify the causes of ALS. (ALS) and identify potential treatments.
Answer ALS is a revolutionary research project co-founded and operated by Johns Hopkins University and the Robert Packard ALS Research Center in the United States and Avanade, with more than 1,000 ALS patients participating research for this project. The project brings together global research centers, industry-leading technology companies and world-class researchers. The large amounts of unstructured data generated by this global collaboration create challenges.
How can researchers effectively utilize this data and gain insights? Tripti explained: “We leverage a cloud computing model with a strong infrastructure for machine learning to create something similar to an Azure-based data query engine that can process research queries in hours instead of the days and weeks of the past. At the same time Researchers can analyze more data faster and use it as a basis to accelerate the development of successful treatments for ALS patients."
Use Case 2: Leveraging Artificial Intelligence and Machine Learning in the Healthcare Supply Chain
Artificial intelligence and machine learning play an important role in the future of healthcare when it comes to improving patient treatment and care. These advanced analytical methods can also be used to help healthcare organizations improve efficiency and address issues such as supply chain challenges, especially during times when the COVID-19 pandemic has exacerbated supply chain difficulties.
Sethi Company, a large pharmaceutical wholesaler, worked with Avanade to improve their error-prone and unreliable inventory tracking methods. Previously, common tracking technologies such as RFID and Bluetooth technology used as weight calculation sensors were unreliable and cumbersome, causing Sethi's profit margins to decline.
To solve this challenge, the collaborative team is combining artificial intelligence (specifically computer vision and post-processing machine learning models) with connected cameras to edge computer nodes and allow cameras to continuously monitor and control the environment in real time. Track inventory changes to help pharmaceutical wholesalers increase profit margins and improve billing accuracy.
Use Case 3: Leveraging Advanced Analytics for Diagnosis and Treatment
Similar to the importance of artificial intelligence and machine learning, advanced analytics will play an important role in the future of healthcare, especially in treatment Discovery, for example, can improve the accuracy of cancer case review, thereby speeding up diagnosis and treatment.
For example, once a cancer patient is diagnosed, the best treatment plan needs to be developed, which requires doctors from different specialties to review and discuss the cancer case, but it is not always possible to get a group of doctors together to It's so easy. To help address this challenge, new collaborative solutions can be enabled that empower staff training and use data analytics to provide insights to physicians and nurses so they can better engage and input their own insights into treatment discovery. "Adding this diverse knowledge helps ensure patients receive the highest quality treatment and care, and hospitals can also speed up diagnosis and treatment times, thereby increasing satisfaction," said Sethi. "Through these use cases, work is being done every day to improve the treatment and care experience, often without the patient's knowledge and without any disruption to patient treatment and care.
Overcoming Ethical Dilemmas
AI-driven algorithms make predictions or generate insights by observing data and learning from it. If that data is biased, the results will be biased as well. Overcoming this ethical dilemma and bias requires accumulating greater diversity dataset, and also requires training artificial intelligence or machine learning algorithms to analyze all data segments.
Sethi said that models can be trained to see all represented data segments and improve the performance of underrepresented groups in the data. Importance. Analysts can extract training samples, re-weight the importance of training samples, and amplify the 'voice' of minority groups." It will also be important for doctors to create explainable and transparent algorithms, so they will be able to understand why a certain These data sets generate certain insights.
Sethi believes that this raises a broader question - why are medical institutions using artificial intelligence and machine learning? “Do we accept the predicted results? Or do we learn from these insights and identify the root causes of healthcare challenges in diverse populations?”
As an example of ethical action, Avanade aims to address ethical or Dilemmas of Responsible Technology, creating a digital ethics framework and applying it to artificial intelligence. The framework creates a checklist for responsible AI, whether focusing on data integrity, privacy, bias or human impact.
The future development of artificial intelligence in healthcare
As artificial intelligence accelerates into increasingly virtual operating environments, it will play a key role in healthcare.
The COVID-19 pandemic has accelerated the shift to virtual care, which has led to an explosion of data. But to keep up with this growth, more can be done to gather insights and drive meaningful change using artificial intelligence, machine learning, and data analytics.
In conclusion, artificial intelligence and big data analytics offer many opportunities for better patient treatment, improved efficiency, and more accurate treatment discovery. We need to leverage these advanced technologies without forgetting ethics, privacy and compliance. The importance of sex.
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