The healthcare industry is amid a significant paradigm shift. Not just driven by innovative treatments and technologies, but also by a radical shift in how healthcare professionals (HCPs) make treatment decisions. HCPs grapple with numerous hurdles including constraints on time and an overload of data from various sources like medical research, patient histories and patient reported data, and pharmaceutical trials.
Traditional decision-making techniques, reliant on individual experience and generalised medical guidelines, are probably no longer the most effective. This unending data stream can lead to 'information paralysis', making it challenging for HCPs to identify relevant and valuable information. HCPs may feel overwhelmed which can result in missed opportunities for the best patient treatment decisions. If HCPs cannot efficiently analyse and process relevant and new data, it could potentially impact the quality of patient care.
To streamline treatment decision-making while maximising time and resource efficiency, healthcare institutions look at Decision Support Systems (DSS) as stand-alone software, or integrated in their IT system such as Electronic Health Record (EHR) systems, or medical devices such as MRIs. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly improving healthcare and showing evidence they can outperform standard risk models in predicting breast cancer1, or predict individual drug responses more accurately in lung cancer2 than is currently possible using genetic features. Industry leaders including Epic Systems, Cerner, Allscripts, and Roche Diagnostics are at the forefront of this transformation expanding the capabilities to improve and personalise treatment decision-making. AI-infused EHR systems are already transforming treatment decisions by predicting patient needs and health trends, automating routine data processing tasks, and enabling strategic allocation of human resources. AI will significantly enhance DSS by facilitating the creation, management, and optimisation of patient-specific treatment plans.
Looking ahead, the integration of AI and generative AI into DSS software will occur in stages:
In the next year, expect to see more extensive AI integration into DSS systems. AI will automate mundane tasks, anticipate patient needs and health trends, enabling more personalised care. It will guide physicians in making the right decision, and leave the ultimate choice to the human. Generative AI in DSS will automate decision guideline generation and AI-driven patient portals will become the standard for patient engagement.
In the next three years, AI and generative AI will become crucial elements of DSS systems. AI will collate and interpret data from a range of digital touchpoints providing a comprehensive view of patients. Generative AI will tackle more complex tasks such as generating detailed medical reports, and improving predictive modelling, further personalising patient care.
In the next ten years, AI and generative AI will entirely revolutionise DSS systems. AI will become the central hub where data is effortlessly integrated, analysed, and applied cohesively. Its role in treatment recommendation and moderation will be profound, ensuring relevance and maintaining compliance with regulations. Generative AI will manage all communication aspects with HCPs, from creating custom patient summaries to handling complex inquiries across multiple languages and cultures.
In conclusion, the future of treatment decision-making will heavily rely on embracing this digital revolution, specifically integrating pioneering technologies like DSS, AI, and generative AI into their strategic planning. As we find ourselves at this pivotal moment healthcare leaders must harness these technological breakthroughs to foster more efficient, personalised patient care.
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