Artificial intelligence is reshaping how medical professionals deliver patient care experience. Medical institutions have grappled with staff shortages, supply chain complications, evolving patient requirements, and other factors impacting patient care delivery. Recent developments in AI, such as deep learning models, brought much-needed respite to healthcare providers. AI improves patient care delivery with efficient, cost-friendly, and automated processes.
With the emergence of generative AI and other deep learning technologies, medical professionals welcome greater possibilities for improving patient care with AI. Advanced AI technologies allow medical professionals to streamline medical processes by leveraging patient data, automation, and predictive analytics. This provides patients access to personalized healthcare, prompt diagnoses, drug availability, and other medical benefits.
Healthcare solution providers should capitalize on the AI momentum in the healthcare space. In 2021, 9% of healthcare institutions used AI models, underscoring the significance of AI in a $11.06 billion market. By 2030, the AI healthcare market is expected to hit $187.95 billion. Innovating healthcare solutions with AI is a thoughtful choice at this critical juncture.
As Uptech’s tech lead, I’m ready to guide you in developing AI healthcare products and navigate the complexity of doing so. Our experience in several AI projects, such as Dyvo AI, Angler AI, and Hamlet, will help support your development efforts. Moreover, I’ll share tech stacks and best practices our team uses to build AI products in this article.
Overall State Of Patient Care Today
Healthcare providers worldwide have recently witnessed significant changes to operational procedures, partly driven by the recent pandemic. Medical facilities and the workforce are stretched to the limit, laying out limitations and uncovering substantial opportunities for improvement. Amidst uncertainties, patient safety remains the utmost priority. Clinicians, nurses, and administrative staff strive to address individual needs while reducing medical risks.
Despite their best efforts, more must be done to mitigate the challenges that medical institutions face. According to the World Health Organization, medication errors caused 1 out of 30 patients to suffer wide-ranges of implications in their stay at medical facilities. Meanwhile, surgical errors stand at 10%, while diagnostic mistakes may reach 20% in patient-doctor interactions.
The alarming statistics reveal gaps in healthcare delivery, resulting in less-than-ideal patient experiences. More importantly, human oversight, which is unavoidable, can cause severe mortal implications. Another survey saw 85% of physicians agree that uncoordinated care is to blame for misinformation by healthcare providers.
Noting the inadequacies, hospitals, clinics, and medical facilities explore ways to improve healthcare delivery. This includes upskilling medical workforces, digitalizing workflows, and investing in state-of-the-art medical equipment. For example, Cleveland Clinic provides patients access to MyChart, a telehealth app allowing them to schedule appointments, consult with physicians, and view diagnosis results.
With AI, healthcare providers can accelerate efforts to optimize patient care experience with better means. Advanced AI models can learn from electronic health data, supporting physicians in decision-making and administrative tasks to benefit patients. Likewise, medical researchers can use AI to accelerate drug discovery, ensuring timely treatment for those in need.
How Will AI Benefit Patient Care?
AI augments existing medical infrastructure, technologies, and staff while uncovering new opportunities to improve patient care delivery. Its capability to analyze a vast amount of readily available medical information allows healthcare providers to overcome existing limits.
I share several potential use cases for AI in patient care below.
Generative AI allows patients to receive personalized care tailored to their needs, pathological symptoms, and preferences. With AI, physicians can analyze patient’s medical history, lifestyle habits, and other demographic data to devise targeted, accessible, and effective treatment plans. For example, patients can request a personalized diet plan from an AI-powered app to aid their post-surgical recovery.
Drug research, discovery, and monitoring
Patients rely on safe and effective drugs to treat illnesses and maintain their well-being. Drug research is a tedious process that costs medical companies considerable investments, resources, and time. Generative AI can accelerate drug discovery by analyzing volumes of medical literature, recommending suitable molecule compounds, devising clinical trials, selecting candidates, and more.
With AI, pharmaceutical companies can establish a seamless supply chain with healthcare providers, enabling patients timely access to potent and safe drugs. More importantly, physicians can use AI to monitor drug dosing amongst patients for signs of complications.
For example, Expert AI uses advanced AI technologies that leverage a comprehensive knowledge base consisting of drugs, biomarkers, symptoms, and geographic locations to speed up drug discovery.
Medical imaging, such as X-rays, MRI, and CT scans, is vital in helping doctors diagnose diseases and prescribe appropriate treatment. Generative AI can improve imaging accuracy and speed, allowing doctors to support their decisions with data-driven predictions. AI-powered medical imaging solutions can detect ambiguous details that humans may overlook. For example, NASNetLarge, a convolutional neural network variant, can detect breast cancer with up to 88.41% accuracy.
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With limited medical staff, healthcare providers need automated assistance to monitor patient movements and recovery journeys in and outside medical facilities. AI can help medical staff keep a watchful eye on patients by gathering, analyzing, and summarizing data from connected medical devices. This way, patients can continue receiving dedicated care with AI monitoring after hospital discharge.
Patient’s experiences are not only affected by the treatment they receive but also by the medical establishment’s administration. AI empowers key stakeholders to make data-driven decisions that eventually benefit patients. For example, AI helps medical professionals overcome misinformation by detecting patient sentiment and delivering appropriate messaging campaigns on chatbots.
What Are AI Use Cases In Healthcare?
AI is a broad discipline that ranges from a simple regression model to its more advanced large language counterpart. Depending on the use case, one or more subdisciplines of AI might be applied.
Predictive analytics enable an AI model to analyze historical data and make future predictions. AI apps enabling medical treatment personalization use predictive analytics and other software technologies. Predictive analytics allow the app to uncover specific patterns from patient data and predict probable outcomes with strong confidence. For example, AI can predict if a patient is at risk of non-communicable disease based on their age, lifestyle, and family history.
Natural language processing (NLP) is a subfield of machine learning that allows computer systems to understand languages like humans do. Drug researchers use NLP-powered applications to extract drug names, lab results, clinical trial data, and other notable findings in medical literature.
Computer vision in healthcare uses machine learning and deep learning models to extract spatial data from medical images. It improves imaging accuracy and shortens the wait time for scan results. Recent advancements in computer vision, such as Meta’s Segment Anything Model, allow machine learning engineers to encapsulate pathological anomalies with just a click. With further refinement, the model can help doctors promptly diagnose and treat diseases, bone injuries, and dental issues.
Movement analytics integrate AI with wearable devices, such as smartwatches and medical bands, to support patient rehabilitation. These devices collect real-time data and guide patients' movement in therapy. The technology minimizes human intervention while enabling patients to work their way to full recovery.
Simulation feeds AI models with hypothetical data to visualize probable scenarios patients could experience. For example, hospital administrators apply a simulation model to forecast in-patient check-in, bed usage, and ward capacity during flu seasons to coordinate medical response efforts better.
Real-Life Examples of Patient Care AI Applications
To elevate patient experience, healthcare providers and medical researchers have applied AI incrementally in various areas.
Formerly IBM Watson Health, balances AI and medical experts to facilitate medical research. Prosciento, a metabolic disease research organization, uses Merative Zelta, an AI-powered clinical data management solution, to improve medical coding speed and accuracy.
It is an advanced AI research lab the search engine giant acquired. DeepMind’s medical AI is applied in Streams, a medical assistant app that the Royal Free London NHS Foundation Trust uses to assess pathological conditions. DeepMind allows clinicians to detect acute kidney infections up to 48 hours earlier than conventional methods.
Babylon Health provides AI-assisted diagnosis by analyzing aggregated and anonymized health data that patients consented to. Babylon partnered with NHS, extending 24/7 primary and chronic care services to patients in the UK.
This app uses FDA-approved machine learning algorithms to analyze medical imaging scans and enable better patient outcomes. Viz is widely used in several medical institutions, including Cooper University Hospital, and is acclaimed for enabling early detection and timely intervention.
This is a platform that allows clinicians to automate cancer biomarker detection with AI technologies. Hospitals and laboratories worldwide, including King’s College Hospital, Memorial Hospitals Group, and Guy's and St Thomas' NHS Foundation Trust, use Paige AI to support their cancer detection workflow.
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Tips on Implementing Generative AI In Patient Care
As generative AI evolves, more technical and ethical challenges unravel, demanding answers from AI and industry experts. Use these tips to navigate known challenges in AI development for medical products.
- Data privacy and security is top priority to safeguard patient’s interest and comply with regulations like HIPAA. Use protectionary measures like encryption and multi-factor authentication to prevent unauthorized access and malicious attacks.
- High-quality datasets are essential in developing high-performance medical AI applications. Low-quality training samples can lead to inaccurate, biased, and inconsistent AI models. Ensure you curate diverse and relevant datasets to train or fine-tune the deep learning model for healthcare use cases.
- Avoid using generative models for arithmetic purposes without a human safeguard in place. Such models are not designed to perform calculations and may produce erroneous results.
- Choose an appropriate AI model that fits the application requirements. For example, a convolutional neural network (CNN) is better suited for medical imaging AI systems, while the transformer’s superior language understanding capability is ideal for AI virtual assistants.
- Assess and refine model performance after deployment. Use anonymized data from subsequent user interactions to improve the model’s inference accuracy and fairness.
How can Uptech help?
Uptech brings skills, experience, and talent to help you develop competitive AI solutions for elevating patient care experience. As an international app development company, our team enables clients to adopt state-of-the-art technologies in the products they offer. At the same time, we take a people-centric approach in what we do – by valuing user feedback in every development stage.
When improving patient care with AI, working with a multidisciplinary team with proven experience is advantageous. Our team built Hamlet AI, a text summarizer with a machine-learning model. We trained a powerful language model to produce high-quality summaries from uploaded documents. We can use similar AI technologies to analyze medical data in patient care products.
Besides advanced AI products, Uptech has also delivered regulatory-compliant solutions for the healthcare space. A notable example is a mental health app we built for the US market. The app connects users with online therapists, providing secure consultations, seamless user experience, and in-app support.
We’re confident that we can replicate the same success for you. Learn how we approach AI development with sensitivity to time-to-market, cost, and proof of concept here.
Generative AI promises a profound transformation in patient care delivery. Whether personalizing treatment plans, aiding the drug discovery process, or streamlining hospital management, AI has much to offer clinicians, nurses, and healthcare administrators.
Several solution providers have successfully launched patient care products, underscoring the growing demand for the efficiency that AI brings. The challenge, however, lies in applying AI responsibly to safeguard patients’ interests. I’ve shared tips you can apply to develop compliant, accurate, and functional AI systems. Still, most clients prefer to work with an experienced development team to reduce development risks.
At Uptech, we share your mission of improving patient’s experience with AI. Talk to us to bring your idea to life.