AI in Healthcare Software: Benefits, Use Cases, & Potential Drawbacks
The healthcare industry is on the verge of a monumental shift driven by advances in Artificial Intelligence. AI is no longer a futuristic concept — it’s actively shaping how doctors diagnose diseases, manage patient care, and optimize healthcare operations. This provides a host of benefits, but also has some drawbacks for the time being. Keep reading to learn more!
Artificial Intelligence & Healthcare
While combining AI and healthcare isn't new (early applications as far back as the 1970s used rule-based diagnostic systems), limitations on processing power hindered its true value and potential. However, more recent innovations like big data and machine learning have enabled AI to provide more and more functionality, even in highly regulated industries like healthcare.
Benefits of AI for Healthcare
This functionality can come with a variety of benefits and improvements (if implemented and utilized effectively) that boost efficiency, empower patients, and enhance clinical decision-making:
Operational Efficiency
Software that leverages AI features and processing can automate repetitive administrative tasks like scheduling appointments, filling prescriptions, processing insurance claims, and generating reports. By doing so, it frees up valuable time for doctors, nurses, and other healthcare providers, allowing them to focus on what matters most — providing high-quality patient care.
Example: A patient is able to easily schedule an appointment through an AI assistant that considers their availability, preferred doctor, past prescriptions, and insurance coverage. This not only saves time for staff but also creates a convenient and individualized experience for the patient.
Improve User Experience
Speaking of individualized patient experience, this concept can extend far beyond setting up appointments. AI assistants can act as the first point of contact for people with questions about basic medical concerns, billing, rescheduling, and more. On top of active questioning, these virtual assistants can provide medication reminders, explain medical charts or scans, and follow up with next steps. This reduces the workload on call centers and overworked doctor’s office staff, which leads to a better overall experience, less frustration, and more integrated reminders for everyone involved.
While this may seem contrary to current experiences with convoluted call menus for banks or pharmacies when you just want to talk to a human, the expectation is that better AI will alleviate this by helping you find what you need faster. In fact, 64% of patients in a 2018 study by Syneos Health Communications reported that they would be comfortable with AI virtual nurse assistants because of the benefit of 24/7 access to help — image how that’s evolved in the years since, with COVID and the explosion of AI exposure.
Example: A chatbot guides a patient experiencing flu-like symptoms through a series of questions. Based on the answers, the chatbot can recommend over-the-counter medications, suggest scheduling a virtual appointment with a doctor if needed, or direct the patient to relevant health information online. This not only empowers patients to make informed decisions, but also reduces strain on the healthcare system.
Increase Accuracy of Diagnoses
One of the biggest advantages of AI/ML algorithms is how efficiently they can analyze vast amounts of complex data — such as a patient's medical history, imaging scans, and lab results. This equips doctors with a better (and quicker) understanding of a patient's condition, potentially leading to earlier and more accurate diagnoses. To back this up, a 2023 study showed that some AI algorithms could detect skin cancer with an accuracy comparable to dermatologists. This not only improves outcomes for patients but also reduces the need for unnecessary biopsies and invasive procedures, minimizing patient discomfort and treatment costs. According to Harvard’s School of Public Health, AI could improve health outcomes by up to 40% and reduce treatment costs by up to 50%.
Example: Another study found that analyzing mammograms with the assistance of an AI system could significantly increase the chances of identifying cancers (without increasing false positives), even spotting subtle signs of breast cancer that may have been missed by the human eye.
Better Health Monitoring
Wearable devices and sensors have been one of the biggest wellness trends of the last decade. They’re also generating a constant stream of real-time patient data like heart rate, blood pressure, and blood oxygen levels. Like the previous section about diagnoses, artificial intelligence can analyze all of this data to identify potential health risks and enable preventative measures.
Example: An AI system monitoring a patient's wearable data detects a sudden spike in blood pressure. The system can then prompt the patient to take their medication, advise them to contact their doctor, or even dispatch emergency services if necessary. This real-time monitoring and intervention capability can potentially save lives in a way that traditional doctors and health monitors simply can’t.
Help Connect Data Points
Continuing the conversation about massive amounts of data, AI not only excels at analyzing huge quantities of data, but also at evaluating data from diverse sources in different formats that would otherwise be difficult to compare. This might include electronic health records, medical research databases, and public health registries. The ability to quickly find hidden patterns and connections across variables and data points can lead to a more thorough understanding of diseases that paves the way for developing personalized treatment plans tailored to each patient's unique needs.
Example: AI analyzes data from cancer patients across many hospitals, identifying genetic markers or treatment responses that may have otherwise gone unnoticed. This collective intelligence can lead to breakthroughs in cancer research and the development of more effective treatment strategies.
Specific Application Areas
The impact of AI innovations in the medical and life sciences field extends far and wide, transforming how healthcare software is used across a variety of applications and use cases:
Disease Detection & Diagnosis
AI-powered analysis tools are at the forefront of medical imaging evaluation. As we discussed earlier, they can analyze everything from mammograms to X-rays to MRIs with high accuracy, flagging abnormalities and potentially leading to earlier diagnoses of diseases like cancer.
Example: A 2022 study found that AI could analyze retinal scans to detect diabetic retinopathy, a complication of diabetes that can lead to vision loss. Early detection via AI-powered software can enable timely treatment and prevent vision loss.
Telemedicine
Chatbots are already playing an increasingly important role in telemedicine. These virtual assistants can conduct preliminary consultations with patients, gather basic information about their symptoms, and even flag urgent cases to doctors, allowing practitioners to get to those who need assistance immediately and to focus on more complex consultations.
Example: A patient is experiencing chest pain and contacts a telemedicine app. A chatbot guides them through a series of questions about their symptoms, medical history, and risk factors. Based on the information gathered, the chatbot can recommend scheduling a video call with a doctor or that the patient seek immediate emergency care.
Electronic Health Records
We’ve talked at length about AI’s ability to analyze vast amounts of data, and this is just as applicable to the information stored in EHRs. This can help identify at-risk patients, predict potential complications, and suggest preventative measures. Even more, AI-powered natural language processing can streamline data entry by converting dictated notes into text — improving the searchability of EHRs and allowing doctors to access patient information more quickly and efficiently.
Example: An AI system logs updates to a patient’s medical profile, analyzing their EHR data as a whole and identifies risk patterns for heart disease. The system can then prompt the doctor to discuss preventative measures with the patient, such as lifestyle changes or medication.
Drug Interactions
Another application of EHR analysis is keeping track of a patient's medications and identifying potential interactions, allergies, or adverse effects. This protects patient well-being and reduces the risk of medication errors.
Example: A new doctor prescribes a medication for a patient, without knowing they’ve had adverse reactions to similar medications in the past. An AI system can instantly check the patient's medication history and flag any potential interactions or allergies, not only confirming the safety of the prescribed medication for the patient, but also its effectiveness.
Administrative Tasks
As mentioned earlier in this article, repetitive administrative tasks like insurance claim processing, appointment scheduling, and report generation can take up hours of healthcare professionals’ days. By having AI-powered systems automate or streamline these, it frees up staff time for more crucial tasks like patient interaction and care coordination.
Example: An AI system uses audio of a telehealth appointment to scribe notes, fill the prescription discussed, and automatically process insurance claims after the visit. All of this vastly reduces administrative burden, enhances record accuracy, and makes for timely reimbursements for healthcare providers.
Drawbacks & Concerns
While the benefits of AI in healthcare are undeniable, there are still factors that hold these systems back from full adoption. These range from patient confidentiality and regulatory compliance to unconscious biases and the loss of human touch and empathy in patient care:
Data Privacy
Because these algorithms rely on huge amounts of data to train and function effectively, robust security measures and strict data privacy regulations are essential for patient information to remain protected from unauthorized access or misuse. Healthcare providers also have to comply with regulations like HIPAA to safeguard patient privacy. These mandate the protection of a patient's identifiable health information and outline the permissible uses and disclosures of this data. However, this can be tricky when AI needs as much data as possible to identify risk factors, integrate with patients’ lives, and more.
Bias
Similar to the data privacy concern, output bias is another issue that comes from AI training data. Algorithms can perpetuate biases present in the data they are trained on, even if they’re unconscious or unnoticed. For example, if an AI system is trained on data where certain demographics are underrepresented, it may lead to biased decision-making in diagnoses or treatment recommendations. Reducing and mitigating bias requires diverse datasets and careful consideration of algorithmic design.
Empathy
AI, while incredibly efficient at data analysis, clearly lacks the human touch of another person. Building trust with patients and ensuring that AI complements — rather than replaces — human interaction is crucial. Doctors and nurses will still be essential for providing emotional support, building rapport, and explaining complex medical concepts to patients. However, AI can be used to enhance communication (which is often reported as the worst part of doctor visits). In fact, studies have found that several AI systems have better bedside manner than human doctors. The key is to combine this communication with human interaction to deliver bad news, diagnoses, emotional support, and more.
The Future of AI in Healthcare
Looking ahead, the future of AI in healthcare is characterized by several emerging trends and predictions. Personalized medicine, more transparent reasoning, remote monitoring and care, accelerated research and medical innovation, and much more are what we can expect from AI in healthcare in the not-so-distant future:
- Individualized Wellness: Personalized medicine, also known as precision medicine, tailors treatments and preventative measures to an individual's unique genetic makeup, health history, and lifestyle. AI can understand the complex network of a patient's genomics, wearable device data, electronic health records, and more to help doctors develop targeted treatment plans and predict potential health risks with greater accuracy.
- Explainable AI: Currently, some "black box" AI algorithms function without clearly explaining their reasoning behind diagnoses or treatment recommendations. Explainable AI (XAI) focuses on developing AI systems that can explain their rationale in a way that is understandable to humans. This will build trust and transparency in AI and allow doctors to make more informed decisions about patient care in conjunction with the AI's insights.
- Integration with IoT Devices: The seamless integration of AI with wearable technology and internet-of-things devices will further personalize medicine and enable real-time health monitoring. Imagine a future where smartwatches equipped with AI can monitor a patient's heart health and detect early signs of arrhythmias, allowing for faster medical intervention.
- AI-powered Drug Discovery: A traditionally slow and expensive process, drug discovery and innovation can be accelerated via AI analyzing vast datasets of molecular structures and identifying potential drug candidates. This has the potential to streamline the development of new medications and treatments for various diseases.
Leverage AI to Enhance Your Healthcare Software
The future of healthcare is undeniably AI-driven. This means that innovation and updates are accelerating faster than ever before, and it’s critical to keep up. The software built today will create the foundation of the healthcare industry tomorrow, and you need powerful tools to accomplish this — manual development simply doesn’t cut it anymore.
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