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Current Challenges for Healthcare Leadership

The main challenges for healthcare leadership (non-exhaustive) include:

Enhanced Decision-Making

AI is capable of providing boards and executive leaders with advanced analytics and data processing capabilities, potentially improving the quality and accuracy of decision-making. Real-time insights from machine learning algorithms may support leaders in making informed and strategic choices. However, the green pastures of analytics and data processing depend, as ever, on the quality of the statistics gathered and the way the data is processed. And we all need to remain aware of a natural bias to find statistics that suit our personal position or argument.

Further, accountability held among individuals, organisations, and AI systems regarding decisions based on support from an AI algorithm is perceived as a risk that needs to be addressed. However, accountability is not addressed if advice turns out to be incorrect. For example, if a patient is given AI-based advice from a county council-operated patient portal for triage-suggested self-care, and the advice instead should have been to visit the emergency department, who has the responsibility? Is it the AI system itself, the developers of the system or the county council? (Peterson et al. 2022). In this respect, laws need to be developed rapidly to ascertain liability and accountability.

Strategic Planning in the Digital Age

Healthcare boards must take into account that strategic change processes are long-term. Executives need to factor in the rapid pace of technological change, digital disruption, and emerging challenges and opportunities in their strategic frameworks. AI-driven analytics may provide a deeper understanding of market trends, customer behaviours, and competitive landscapes, enabling healthcare leaders to formulate agile and adaptive strategies. However, the potential lack of intuition and creativity in AI systems is a concern, particularly in fields that require innovative thinking.

The change process design is a crucial task for strategic leadership, and careful use of AI in the planning phase may provide a more in-depth, real-time and accurate business environment and competitive analysis for the orientation of a visioning process and strategic implementation. However, before any AI soil is tilled and seeds planted, leaders must reach a common understanding of AI itself (AI, machine learning and deep learning) to define an objective for how AI can be applied in the future (Pokorni et al. 2021).

Healthcare leaders also have the task of communicating and demonstrating the vision, objectives and strategy, including the use of AI in that process. Communication strategy must be inclusive to have any chance of success - employees, employee representatives or external stakeholders must be included in the formation of the change process and will be one of the key success factors. Through participation, stakeholder doubts and concerns may be addressed and included during the design process and along the implementation milestones. Stakeholders should understand both the why and how they may participate, and transparent discussions on proposed designs of AI hardware and software systems are crucial to have any chance of seamless technological integration with partners.

Cultural Integration

Successful integration always involves a shift in the cultural paradigm of thinking, mindset, attitudes, behaviours, and knowledge acquisition. The challenge lies in balancing the implementation of new technologies with the preservation of core organisational values, and the chance of unintended consequences derailing a cultural change is high.

Both for healthcare leaders and stakeholders, new technology is one of the most frightening aspects of strategic change involving, as it does, substantial economic and capital commitments and implementation decisions with far-reaching implications for growth and success. Usually, a very steep, innovative and continuous learning curve has to be culturally assimilated, and the cultural expectation that ‘this is the way we do things around here’ may be fragmented, in some cases, almost beyond recognition. This may lead to heavy resistance and reticence to adopt AI, the severity depending on the organisational culture and success, or lack of success, of communication strategy. In the rather hierarchical, bureaucratic and silo organisational culture prevalent in the healthcare sector, championing a digital mindset and creating an environment conducive to experimentation and learning may be extremely challEnge