Tired of hearing about generative AI? We feel you! Let’s shift the focus to real impact.
Sure, generative chatbots are incredibly common; we all think we’ve seen it all. But there’s so much more the technology can do for healthcare companies, transforming patient care, communication, and convenience while making doctors' and nurses’ work less demanding each day. And if yours is a healthcare tech business, smart bots can speed up problem-solving and accelerate innovations – provided they are in the right hands!
Ready to get inspired? Here are four unexpected ways generative AI is transforming healthcare and how augmented tech teams help make it happen.
When it comes to gen AI in healthcare, there are no ifs. It’s here, and it’s changing everything. With 70% of healthcare leaders, from hospitals to tech providers, already on board, the real question is: how to make the most of it? And who’s the right team for the job?
We’ve got answers to both. But let’s tackle the use cases first.
In medicine, prevention beats intervention, and here’s a handful of examples to illustrate that:
Predictive patient outreach stems from research like this, identifying patients needing extra attention and encouraging them to take action before things get serious.
For instance, a hospital may use machine learning algorithms to analyse data about discharged patients. Then, it can proactively follow up with personalised advice, tests, screenings and resources to lower the risk of readmission. Or a clinic can rely on predictive modelling to monitor patients’ risks of developing chronic illnesses, based on a compound of individual historical data and cohort statistical predictions.
And while generative AI isn’t a must-have for these, it can greatly enhance the interactions with vulnerable patients while taking some tasks off from humans.
AI-powered generative bots can enable faster, personalised communication, getting instant access to patient data and providing efficient first response. They can send reminders about check-ups and appointments, nudge patients to take medication or assist them with lifestyle and well-being tips that can save them a doctor’s visit. On a more complex level, gen AI can reach out to patients with identified risks to refer them to a specialist or arrange an appointment or a lab test.
Talking about personalisation, precision medicine is a significant area for generative AI exploration. Highly-targeted drugs represent just one aspect of delivering personalised healthcare. Applying analytical and cognitive capabilities, gen AI tools can match drug molecules to each patient’s specific genome, health history, and molecular profile, enhancing the effectiveness of medical treatment.
Advanced models are also driving personalised treatment plans. By understanding a person’s profile and lifestyle and embracing their treatment and medication history, generative AI can suggest the most effective therapies. Additionally, advanced models can predict the most likely outcomes by modelling and simulating disease progression, helping patients better manage their condition in the future. They will know what to expect and how to prepare, which can also help reduce the mental burden associated with the diagnosis.
In radiology, generative AI enhances the accuracy of individual-focused diagnoses. Attempting to read a CT or MRI scan without years of specialised medical and radiology training is pretty much impossible. But despite radiologists being among the top-paid medical professions in the USA and Europe, among other regions, they can still get stuck if the images they receive are incomplete or blurry. Luckily, generative AI has some answers.
By analysing tons of data and applying predictive models, technology can fill up missing or unclear bits and provide accurate readings. It taps into detailed patient information to help technicians make better assessments, considering individual differences like unique body shapes or slight changes from past injuries.
A few notable examples from Microsoft’s Open AI illustrate how gen AI can assist healthcare organisations in promoting patient-centric care while alleviating the workload of their staff.
One compelling use case of generative AI in action is a med-tech platform that supports over 30 languages to meet the diverse needs of patients internationally. Generative models also automatically align platform users with healthcare services they need automatically, based on their enquiries and treatment history.
In another instance, the Open AI technology underlies a smart, personalised patient assistant that significantly improves self-service capabilities. By analysing patient context, it replies to patient enquiries without human intervention. One key application is prescription generation. Thanks to its generative capabilities, the system issues over 180,000 prescriptions monthly to patients in India – all without requiring them to step outside their homes.
While this may seem like a specific case, no healthcare system can thrive without it. Medical summaries are the backbone of effective and personalised treatment, offering a comprehensive view of a patient’s medical history. Yet, this essential task often becomes an unwieldy burden for many physicians. They often spend more time completing patient documentation, taking notes, scanning test results, and checking diagnostic information than engaging meaningfully with patients. This excessive manual documentation is a key contributor to physician burnout, extending work hours and diverting attention away from patient care.
Fortunately, in today’s modern healthcare setting, doctors are already saving time on notetaking with automation. Generative AI offers the exciting opportunity to reduce this load even further.
By enhancing traditional Natural Language Processing (NLP) with advanced generative models, gen AI solutions can truly grasp the content and context of medical notes, treatment plans, and diagnostic conclusions. Rather than following pre-set rules, they go a step further. While simpler solutions can effectively extract keywords and provide template summaries, their more refined counterparts create coherent summaries enhanced with crucial details pulled from indicated sources. This means they not only extract and sum up data; they craft summaries that provide deeper insight.
Moreover, Gen AI can adapt the tone, voice, and style of the summaries and standardise terminology, medical codes and abbreviations, ensuring all providers in patient care understand them, which helps cut tedious back and forths. They can also transcribe speech to text and translate it into any language, offering immense value for international medical teams.
What feelings can you convey in a short text message? Any. Even a misplaced comma or an exclamation mark can say a lot, not to mention the impact of emojis or creative formatting.
Sentiment analysis, which uses AI to determine the emotional tone of written text, is used across industries, especially in marketing. Now, it has made its way to healthcare, too. By looking closely at what patients say in feedback forms and satisfaction surveys, providers can gauge patients' satisfaction and find improvement areas. Analysing clinical notes analysis and patient comments can also identify patients with the risk of depression or other mental health issues. Plus, diving into clinical trial feedback can give in-depth insights into how participants really felt about a medication.
Traditional methods like natural language processing and machine learning are often used in healthcare to assess patient feelings. But generative AI takes it a step further. It doesn’t just use statistical data or keywords and rules to come up with conclusions. Instead, it brings rich insights from various data sources, understands context, works across languages, and analyses data in real time. With continuous learning capabilities, it helps uncover more nuanced, accurate, and actionable insights that lead to better outcomes.
To really make the most of generative AI, whether you’re a medical facility, research centre or MedTech provider, you need three things: AI expertise, a solid grasp of the medical field, and stability. Knowing how to build AI and machine learning solutions is essential. But not enough on its own.
Truly meeting patients’ and doctors’ needs with empathy and care requires a team that understands healthcare. A team familiar with its specific regulations and challenges. Relying on a patchwork of freelancers isn’t the way to go!
Dedicated tech teams may be your best bet. Selected from a global network of seasoned professionals through a tailored recruitment process, they bring the right mix of technical and soft skills and valuable healthcare experience. They’ll fit right into your business, working on your terms and schedules and making a noticeable impact while operating seamlessly from the admin perspective.
And as opposed to hiring internally or engaging outsourced freelancers or teams, dedicated teams pose no risk. They involve no upfront fees, hidden costs, or lock-ins, making them a stress-free option for the healthcare industry. Our goal is to provide the right talent to integrate generative AI and advanced tech into your solutions. With above-average retention of 95.7%, they’ll become your long-term trusted resource.
Our successful partnership with Televic Healthcare is a prime example of this approach in action. Using our unique framework, we assembled a skilled team of professionals who became our client's internal R&D powerhouse in a short time. They took on critical tasks such as UI design and development, microservice architecture migration, and seamless integration with mobile platforms to build some of the company’s core innovative healthcare products. These included an advanced nurse call system connected to healthcare devices or IoT wandering detection technology integrated with IoT bracelets that ensure patient safety without needing 24/7 oversight.
If you’re ready to start or want to learn more about how dedicated teams can help you achieve more, get in touch!