Across the entire healthcare continuum, patients and practitioners are benefiting from machine learning (ML) and the way in which it streamlines day-to-day treatment processes.
From bedside care to data mining and beyond, cutting-edge ML technology is helping healthcare specialists develop intelligent operational models. These models are making smart medical treatment possible and resulting in reductions in waiting times and improvements in clinical accuracy.
The benefits of ML-augmented software for healthcare go beyond care, diagnosis, and
Machine learning actively helps stakeholders monetize the field of medicine by automating repetitive work, increasing overall workforce productivity, and optimizing the use of finite clinical resources.
There is now a significant call for ML-augmented tools and practices. Let’s delve deeper into the benefits of this technology and take a closer look at how it can be integrated with electronic medical records (EMR) software, hospital information system (HIS) software, and health information exchange (HIE) software.
ML Healthcare Software Enables Proactive Care Transition
Medical clerks and secretaries are tasked with regulating the flow of patients entering and leaving their healthcare facilities. When faced with surges in patient demand, these healthcare industry professionals must be able to anticipate footfall and, in turn, adapt to rapidly-changing environments and circumstances.
Machine learning facilitates the predictive analysis process, helping to alleviate potential patient journey bottlenecks before they arise. This allows medical administrators to initiate proactive care transition protocols no matter how many patients require medical attention on any given day, a crucial capability given today’s ever-expanding global population.
Medical administrators can gather, store, and extract real-time and historical hospital data by leveraging machine learning algorithms. This allows the administrators to accurately forecast patient flow and care requirements over a 24- to 48-hour period, which in turn is what enables doctors to provide urgent care to those who need it most. From admission to discharge, patients are provided with the exact level of care they require, exactly when they require it.
Machine learning (ML) algorithms can be updated periodically, making this technology beneficial to administrative staff members in hospitals. With ML embedded in healthcare analytics software, clerks and secretaries can track the latest health trends and conditions and store that information digitally.
ML Healthcare Software Facilitates Unobstructed Diagnostics and Decision Making
Doctors and medical professionals require access to real-time diagnostic data at all times. This data is what allows them to make timely and effective care decisions. The speed at which they’re able to extract and utilize this information can profoundly affect the ongoing well-being of the patients in their care.
Machine learning actively aids in the clinical decision-making process by performing the following tasks:
- Extracting standardized data via the quickest possible data mining curve, reducing the time, effort, and money required to augment patient status
- Automating the extraction process to ensure that busy hospital staff members aren’t required to perform tedious data measurements
- Digesting heterogeneous data to recommend specific interventions for patients who belong to certain medical groups
Machine learning is a subset of artificial intelligence (AI), and the company, DeepMind, is using AI tools for medical advances. For example, DeepMind developed an AIpowered diagnostic method that it describes in its article, Using AI to give doctors a 48-hour head start on life-threatening illness. The July 31, 2019 article appears on DeepMind.com and states that the company was able to use AI to speed up the process of predicting whether acute kidney injury would occur in patients.
With this type of critical information at their disposal, clinicians can monitor patient deterioration, recognize and review diagnosis patterns, and predict the onset of life-threatening ailments. According to a study conducted by an established healthcare foundation, this provides clinicians with an extra two hours a day to perform their faceto-face patient care duties.
Optimizing ML Healthcare Software with Custom Integration Support
Quality, value, and outcome aren’t just buzzwords when used in relation to the healthcare industry. They’re the three pillars of successful care that all medical professionals abide by and aim to provide at all times.
To deliver on these promises, more and more healthcare specialists are now choosing to augment their skill sets with ML-enabled healthcare software.
This provides healthcare software developers and proprietors with a fantastic opportunity to scale their resources, attract new audiences, and establish their brands within the modern digital landscape.
Want to seamlessly integrate ML into your existing EMR, HIS, or HIE system? If so, you should consider aligning yourself with a machine learning augmentation expert who specializes in healthcare software development.
In addition, the right software partner will consider HIPAA compliance during development, and HIPAA-compliant software can help medical providers maintain patient privacy and avoid costly fines.
Expert-level software developers will also be able to engineer robust HL7 interfaces that facilitate streamlined data sharing and standardized clinician communication.
With the right custom integration support provider by your side, you can create a robust machine learning system capable of recognizing community-wide health patterns, automating prescription enablement, optimizing triage prioritization, and improving diagnostic decision analytics. As a result, you’ll save time and money.
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