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The healthcare world is always finding itself in a constant state of change, upgrading its services to better meet the needs of patients and partners alike. Thanks to COVID-19 and all of the complications that spiraled off of that event, healthcare partners were forced to suit up and take this catastrophe by its horns to ensure that they could respond in an appropriate and also in an efficient manner. The Delta variant continues to populate patients into already-crowded hallways and rooms, and many organizations are experiencing a staffing shortage and are coming up short in providing care. Machine Learning (ML), however, can draw on historical data sets, recorded from earlier days of the Pandemic’s initial peak, to predict patient symptoms and outcomes by the masses. Outside of that, on an administrative side, ML is vastly present in healthcare environments to ensure that the best level of care is properly delivered to those in need.
Improving the Quality of Care
Machine Learning in Healthcare (MLH) essentially aims to predict some clinical outcome on the basis of multiple points. The number of ML-based tools and resources that are adopted within any clinical environment reflects only a fraction of the investment into the field as a whole, suggesting that most applications have not progressed very far past their initial stage. In an attempt to cut out on that downtime and those wasteful research methods, reporting guidelines serve a very critical purpose in such clinical translations. Although analysts can typically use ML to obtain impressive results quickly, but these methods can incur costs of data curating, testing, updating, and governing these tools over time.
Machine Learning’s premise lies in its ability to leverage health informatics to predict health outcomes through predictive analytics, leading to more accurate diagnoses and treatment plans, mastering a physician’s ability to access valuable insights for personalized care going forward. Such benefits can for this method can include: Improving efficiencies for the operational management of healthcare business operations; Enhancing the accuracy of diagnosis and treatment in personal medicine procedures; Increasing valuable insights to enhance cohort treatments through tools such as remote patient monitoring collaborative techniques.
Taking Care of Administrative Tasks
The average healthcare professional sifts through hundreds of pages of data on a daily basis, coming in the form of patient records, symptom sheets, and financial/payment information that there is definitely a need to control all of that incoming information in a better organized fashion, and machine learning and provide that ease. Gaps in healthcare data can result in ML algorithms slipping up and making faulty predictions. This can be a common occurrence if data is not handled effectively. The need to prepare these pockets of data accurately is critical when it comes to accessing it the best way. The professionals working all throughout the day in these roles are in charge of exhibiting the right level of integrity for all of the data that needs collecting, analyzing, classifying, and cleansing before it’s passed onto its next step.
With all of those important data points coming in, it’s important to maintain upkeep centered around ensuring that those records are kept safe and sound. Machine Learning can help to streamline record-keeping processes, including all instances of Electronic Health Records (EHRs). At the same time, Artificial Intelligence (AI) can improve EHR management by improving patient care, reducing healthcare and administrative costs, and optimizing other operations as much as possible; this can include providing support for clinical decisions, automating image analyses, and integrating tele-health methods and technologies.
Machine Learning, Ethically
But these systems can be biased, just like their human creators. A 2019 study published in Science found an algorithm was significantly less likely to refer patients from different ethnicity groups, that aimed to improve care for patients with complex needs. A research letter in JAMA noted that U.S. patient data algorithms were mostly pulling information from cohorts in California, Massachusetts and New York, which wouldn’t be representative of patients living in other areas. This is why it’s very important for the staffed healthcare workers to always take the time and effort whilst using machine learning tools, from an ethical standpoint.
When evaluating machine learning, researchers have suggested that stakeholders be careful about hype, find thorough evaluations of the technology, put serious effort into correcting biases, and demand transparency in data collection and evaluation.
