Artificial intelligence (AI) will transform the clinical workflow with the next-generation technology

By Dilip Nath, DBA, MBA, CHCIO, CDH-E, AVP & Deputy CIO, Downstate Health Sciences University

Introduction

AI is rapidly becoming a game changer with its next generation technologies in improvising clinical workflows, promising enhanced diagnostics, personalized treatment plans and optimized healthcare operations. From automation in diagnostics to predictive analytics, AI has the potential to revolutionize every aspect of patient care delivery.

AI can mean different things, from specific kinds of AI, like machine learning, to the possible AI that has awareness and feelings.

AI in Clinical Diagnosis and Decision-Making

The use of AI algorithms is quite versatile in automatic diagnostics of different diseases simply by analyzing medical images like X-rays, MRIs, and CT scans. These AI systems can now discover patterns and anomalies that may not be visible to the naked eye. It leads to detecting cancer, cardiovascular problems and also neurological disorders earlier.

AI in breast cancer diagnosis is one of the common instances. AI algorithms have been built that can read a mammogram and detect cancers with a high accuracy, often better than human radiologists. For example, an AI system has been able to detect cancer of the breast with 90% accuracy, which is more than 87% compared to radiologists.

AI will be the challenge of humans who should show wisdom and willingness to define the AI’s role in twenty-first-century healthcare and to determine when AI helps humanity and when it hurts it.

AI clinical decision-support is a kind of medical application, which is based on the integration of big data and machine learning (ML) to give personalized medical advice. Employing patients’ data, including history of illness, genetics and determining the response, is the AI system that recognizes risk factors, predicts outcomes and provides personalized treatment plans. The AI breaking into electronic health records (EHRs) is a source of data upload, input and retrieval. It is also an analysis that helps healthcare providers in making decisions and this can be done quickly.

“AI will transform the healthcare sector, particularly diagnosis in the field of medical imaging.”

AI in Treatment Planning and Delivery

AI provides automated dosage calculations and medication management through algorithms that determine the most suitable dosages for patients based on their unique features and medical history. By having automated calculations, AI contributes to reducing the risk of dosage errors committed by people, helping to guarantee that patients are given the exact medications in adequate amounts. 

AI-empowered robotic systems are driving a paradigm shift in surgery through increased accuracy, stability, and reduced people mistakes during surgeries. These robotic systems help surgeons see better and enter into areas of the body that are impossible to reach for normal human beings, thus making it possible to conduct further complicated surgeries using a minimally invasive approach.

AI-infused wearable devices and sensors allow healthcare providers to monitor patients at a distance and provide telehealth services by gathering continuous data regarding patient’s health status and intervening with this data as needed. These gadgets can track or measure vital signs, activity level, drug compliance, and other health metrics and give experts a way to control patients remotely.

A world-renowned company in cancer treatment technologies, has introduced an AI-powered treatment planning product. This product employs machine learning (ML) algorithms to design effective radiation therapy treatment plans based on patient’s past information, thereby substantially reducing planning time and degrees of inaccuracy.

AI in Clinical Workflow Optimization

AI can, in many ways, speed up insurance claims and billing processes through automating the verification of insurance coverage, accurately coding medical procedures, and submitting claims to the insurance company. AI algorithms can utilize medical records, treatment codes, and insurance policies for claim processing and ensure the accuracy that may lead to claim denials or delays.

AI predictive analytics plays a major role in forecasting patient volume, staffing needs, and healthcare facilities as far as resource allocation is concerned. By investigating historical data, patient trends, and external factors, AI algorithms help to predict future demand for health services. Thus, healthcare organizations can reallocate staff, optimize resource usage and improve operations planning.  
For instance, a leading AI-based workflow solution is capable of automating and optimizing several radiology workflows. It deploys machine learning (ML) for automating functions such as image work, finding lesions and report creation, improving radiologists’ efficiency and productivity.

Challenges and Considerations

“AI will be the challenge of humans who should show wisdom and willingness to define the AI’s role in twenty-first century healthcare and to determine when AI helps humanity and when it hurts it”.

The key challenges and considerations in implementing AI in healthcare, including data security and privacy, are the most essential aspects of consideration, as patient’s data can be leaked and AI processing these data leads to the loss of patient trust. In addition to adopting regulations like HIPAA, clinical settings must guarantee the secrecy and safety of private patient information. Clinical decision-making entails the ethical use of AI in AI deployment, which is also a very complex issue. The visible and honest ways of reaching the decision by AI systems must be guaranteed, and it is important to get rid of bias and prevent unfairness in such scenarios to avoid discriminating results. The complex integration of AI technologies into the present healthcare system and workflow flows is the key technical and organizational problem to resolve. Interoperability between AI systems and EHRs gets involved in effective and integrated healthcare provision.

Conclusion

Finally, the use of AI along with clinical workflows presses several benefits in providing patients the best care, improving operations, and advancing medical outcomes in the long run. Yet, the challenges that need to be thoroughly considered are data security, ethical issues, and systemic integration. These are vital factors that must not be disregarded in order to fully harness AI in healthcare.