Work-life balance is a decisive factor for nurse retention, yet scheduling often leaves staff with limited autonomy and recurring dissatisfaction. Insights from German-speaking Switzerland capture how nurse leaders, permanent nurses and temporary nurses perceive fairness, transparency and balance in everyday rostering and how artificial intelligence could address persistent pain points. Participants reflected on current processes across acute hospitals, home care services and nursing homes, outlining priorities for equitable distribution of unsocial hours, stable patterns and clear communication. They also considered opportunities and risks of AI-supported systems that none had used, emphasising the need for human oversight, reliable data and participatory design so that technology aligns with professional values and service realities.
Current Scheduling Realities and Their Impact on WLB
Scheduling is resource-intensive for leaders and a frequent source of frustration for staff. Fixed shift systems are common in intensive care, while general wards use standardised but somewhat flexible approaches. Equitable distribution of early, late, night and weekend duties remains difficult, particularly with fluctuating monthly workloads and last-minute changes. Long-term absences tend to be tracked manually, and short-notice gaps are often filled informally, which many view as opaque and inefficient. Self-scheduling has not eliminated tensions because unpopular shifts still concentrate on the same people.
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Differences between permanent and temporary staff shape perceptions of fairness. Temporary workers typically declare availability and therefore experience more autonomy, whereas permanent staff follow fixed patterns that may not reflect evolving needs. Respect for fixed days off and leave requests is common, but consecutive days off are not always guaranteed, limiting recovery and undermining work-life balance. In private home care, the absence of assured consecutive rest days was cited as a particular strain.
Contextual features of the Swiss system add pressure. Full-time staff work 42 hours per week, most nurses are part-time, and average shifts last 8.24 hours with rosters expected around four weeks in advance. Within these parameters, inconsistent practices and limited transparency heighten perceptions of unfairness. Leaders recognise the administrative burden of coordinating teams and departments, while staff highlight the personal costs of unpredictable patterns and repeated allocation of undesirable shifts. Across settings, the cumulative effect is dissatisfaction among permanent nurses and an ongoing struggle to reconcile personal needs with service coverage.
What Nurses Want from Scheduling
Participants converged on the need for flexible, transparent and equitable rostering that accommodates preferences while meeting organisational constraints. Regular working days and stable patterns were seen as strong contributors to balance. Childcare featured prominently, with clear calls to reflect caregiving responsibilities and preferred shift models in planning. Two consecutive days off, fair allocation of nights and weekends, and at least two free weekends per month were viewed as important. Timely publication, roughly a month ahead, enables planning beyond work.
Preferences for shift length emphasised choice over a single model. Some hospitals combined 12- and 8-hour shifts, particularly on weekends, to support continuity and offer options to staff. Concerns about sustaining concentration over longer duty periods persisted, reinforcing the need to match patterns with individual capacity and role requirements. In home care, fairness encompassed the distribution of case complexity, so no nurse consistently manages the most demanding clients.
Participation and clarity underpinned the requirements. Employees wanted preferences acknowledged within transparent rules for distributing unsocial hours. Stability, minimal last-minute changes and accessible shift swaps were prized. Regular communication and feedback mechanisms were viewed as practical ways to align rosters with personal needs while maintaining coverage. Organisational flexibility was also important, including adjustable start times, movement between departments and occasional unpaid leave, to better match demand with the realities of a predominantly part-time workforce.
Expectations and Trade-offs of AI-Based Scheduling
AI was viewed as a potential means to address long-standing pain points if implementation retains human control and reflects local context. Participants expected AI to process complex constraints more quickly and with fewer errors than manual or purely rule-based systems. Anticipated benefits included time savings for leaders, reduced administrative load, and more consistent application of predefined criteria that could improve perceptions of fairness. Desired capabilities included integrating individual preferences, qualifications, supervisory responsibilities and education levels, as well as accommodating unplanned absences and fluctuating monthly or annual balances. Straightforward mechanisms for corrections, documenting student supervision days and filling short-term gaps were considered essential.
At a strategic level, participants imagined AI as a neutral allocator generating a baseline plan for human refinement. The promise of neutrality could reduce interpersonal tensions by demonstrating that difficult decisions follow clear criteria. If those criteria are visible and justifiable, transparency could improve across teams and settings. In home care, there was interest in more even distribution of clients, aided by the system’s ability to store and reuse prior scheduling information.
Concerns were equally clear. No system could satisfy all preferences, and there was unease about over-reliance on technology. Effectiveness would depend on complete and accurate input data, with technical failures and data quality problems posing obvious risks. Above all, nurses and leaders emphasised irreplaceable human judgement in recognising emotional or physical strain, mediating competing needs and interpreting situational context. They wanted human planners to retain final responsibility for rosters so that department-specific requirements and individual circumstances are respected. Digital competencies, leadership engagement and participatory co-development were identified as critical enablers to ensure tools are usable in real workflows and uphold professional values.
Methodological choices helped surface these perspectives. Focus groups encouraged dynamic exchange across roles and care settings, and knowledge mapping increased transparency by making the analysis visible in real time. Limitations included convenience sampling, online delivery, potential group dynamic effects and the absence of full transcription, but participants articulated a consistent priority: use AI to handle complexity and support equity, while embedding governance that centres human oversight, clear rules and transparent communication.
Nurses and leaders across hospitals, home care services and nursing homes see potential in AI-supported scheduling to deliver fairer, more transparent and more efficient rosters when used to complement, not replace, human decision-making. The shared priorities are stable patterns, equitable distribution of unsocial hours, timely schedules, and sensitivity to childcare and other personal needs. AI can help by processing multifactor constraints, reducing errors and administrative burden, and providing a neutral baseline plan. To realise these benefits, solutions should be co-developed with end users, rely on accurate data and remain subject to human control. A balanced model that couples technological efficiency with participatory processes can strengthen work-life balance, sustain job satisfaction and support retention without losing the human elements that underpin safe and ethical care.
Source: BMC Nursing
Image Credit: iStock
