Artificial Intelligence and Management Control in Hospitals: A Critical Review and Conceptual Integration

Artificial Intelligence and Management Control in Hospitals: A Critical Review and Conceptual Integration Yousra Nassou  PhD in Management Sciences, ENCG de Tanger, Morocco Kamar Moukadem PhD in Management Sciences, ENCG de Tanger, Morocco Abstract This article provides a critical review of the literature on the integration of artificial intelligence (AI) into hospital management control systems (MCS). The rapid development of AI in healthcare has created significant opportunities for enhancing forecasting, optimizing resource allocation, and improving costing accuracy. Despite these advances, the adoption of AI within financial and strategic control frameworks remains limited. The review synthesizes empirical findings and conceptual contributions published between 2020 and 2025. It examines applications such as predictive analytics for patient flows, digital twin simulations for resource optimization, and Time-Driven Activity-Based Costing (TDABC) for more precise financial monitoring. These findings are analyzed in relation to established management control frameworks, including the Balanced Scorecard (BSC) and Performance Management Systems (PMS). The literature reveals a strong convergence on AI’s technical potential, particularly in improving predictive accuracy and operational efficiency. However, divergences remain regarding the extent to which these tools are integrated into governance and financial control systems. Methodological limitations, including reliance on single-site studies, absence of causal designs, and fragmented approaches to prediction and costing, restrict the strength of current evidence. This article proposes a conceptual model that links AI capabilities with MCS and highlights governance as a key moderator of outcomes. It emphasizes that AI does not replace management control but augments it, moving from retrospective reporting toward proactive and simulation-based governance. By identifying theoretical gaps and outlining a future research agenda, the study contributes to a better understanding of how AI can support financial sustainability and strategic decision-making in hospitals. Introduction Hospitals today face growing financial and organizational pressures due to rising healthcare costs, chronic disease prevalence, and resource constraints [31]. In this environment, management control systems (MCS) have become essential tools for aligning strategy with performance measurement, budgeting, and resource allocation. [10] emphasized through the Balanced Scorecard that management control is not limited to financial indicators but must integrate patient, process, and learning perspectives to improve organizational performance. Yet, empirical studies show that many hospitals still struggle with fragmented data and lack the predictive tools needed for effective cost control [13]. The development of artificial intelligence (AI) has generated strong interest for hospital management. [8] demonstrated, using a large hospital dataset, that machine learning models can significantly improve the accuracy of length of stay (LoS) predictions, supporting more reliable planning of bed occupancy and staffing. [16] introduced the concept of the digital twin in healthcare systems, showing how simulation tools can anticipate patient flows and optimize operating room schedules. On the financial side, [13] confirmed that Time-Driven Activity-Based Costing (TDABC) provides More Information How to cite this article: Nassou Y, Moukadem K. Artificial Intelligence and Management Control in Hospitals: A Critical Review and Conceptual Integration. Eur J Med Health Res, 2025;3(3):218-25. DOI: 10.59324/ejmhr.2025.3(3).33 Keywords: Artificial intelligence, Hospital management control, Balanced Scorecard, TDABC, Healthcare governance. This work is licensed under a Creative Commons Attribution 4.0 International License. The license permits unrestricted use, distribution, and reproduction in any medium, on the condition that users give exact credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if they made any changes. EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 219 more accurate cost information in healthcare and argued that digital technologies can facilitate its implementation by automating time data collection. However, the literature also highlights persistent barriers. [17], in their systematic review, identified recurring obstacles such as low data quality, lack of interoperability, and professionals’ resistance to algorithmic tools. [14] further underlined that organizational culture and governance strongly influence whether AI initiatives in hospitals succeed or fail. Beyond these operational and cultural barriers,[5] noted that most AI applications in healthcare have focused on clinical decision support and operational optimization, leaving the integration of AI into management control and financial governance underexplored. The objective of this article is therefore to provide a critical review of the literature on AI in hospital management control. Our approach is integrative and critical, synthesizing empirical findings, conceptual contributions, and management frameworks to highlight strengths, weaknesses, and blind spots. Building upon classical frameworks such as the Balanced Scorecard [10] and TDABC [9], while incorporating recent findings from AI applications in healthcare [8,16], we propose a conceptual model that links AI capabilities with hospital MCS. Theoretical Framework: Hospital Management Control Systems The role of MCS in hospitals is to provide reliable mechanisms for aligning clinical activities with strategic and financial objectives. According to [3], management control can be defined as the process by which managers influence members of an organization to implement strategies. [10] expanded this perspective with the Balanced Scorecard (BSC), which integrates four dimensions—financial, customer, internal processes, and learning and growth—to capture both economic efficiency and service quality. In healthcare, the BSC has been widely adopted as a tool to improve organizational performance. [2], for example, reviewed BSC implementations in hospitals and found that it strengthens performance monitoring, particularly when financial and non-financial indicators are combined. Another fundamental pillar of hospital MCS is cost accounting. Traditional systems based on standard costing or departmental allocations have often been criticized for their inability to reflect the real consumption of resources in clinical pathways. [9] proposed the Time-Driven Activity-Based Costing (TDABC) method as a more accurate and adaptable approach. [13] applied TDABC in healthcare and demonstrated that it allows for a more precise allocation of costs by procedures and services, providing managers with actionable information to reduce inefficiencies and support pricing decisions. In addition to costing, budgeting and variance analysis remain central tools of hospital control. However, several studies point to their limitations in highly dynamic healthcare environments. [1], for instance, showed that rigid budgeting practices in Swedish hospitals often conflicted with the need for flexibility in responding to patient demand, leading to a gap between strategic objectives and operational realities. This finding remains relevant today, as hospitals continue to face unpredictable variations in admissions and treatment costs. More broadly, management control in hospitals must be understood as part of a performance management system (PMS). [6] conceptualized PMS as a holistic framework that links objectives, measures, targets, and feedback loops. In the healthcare sector, studies confirm the value of such integrated systems. [7], for example, demonstrated that when PMS are linked to activity-based costing data, they improve transparency and decision-making at the departmental level. Similarly, [2] emphasized that balanced scorecards, when integrated into PMS, enhance accountability by combining efficiency metrics with quality-of-care indicators. Contributions of Artificial Intelligence in Healthcare and Links to Management Control Artificial intelligence (AI) has emerged as a transformative force in healthcare, offering tools to improve prediction, optimization, and cost measurement. While most applications have been studied in clinical or operational contexts, their implications for management control systems (MCS) are increasingly recognized. Predictive analytics for patient flow and costs A major application of AI in hospitals is predicting patient admissions and length of stay (LoS). [8] demonstrated that machine learning models significantly outperform traditional statistical methods in predicting LoS, enabling hospitals to better anticipate resource needs. Improved forecasting of LoS and admissions not only supports clinical planning but also provides management controllers with more reliable inputs for capacity budgeting and cost variance analysis. [15], in a systematic review of LoS prediction studies, confirmed that AI models (especially gradient boosting and deep learning) consistently achieve higher accuracy compared to logistic regression. They concluded that these tools could be integrated into hospital information systems to enhance both clinical scheduling and financial planning. Optimization of scarce resources Beyond prediction, AI also plays a crucial role in optimizing the allocation of resources such as beds, EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 220 surgical theaters, and nursing staff. [16] highlighted the potential of digital twin models, which replicate hospital processes virtually and allow managers to simulate different resource allocation scenarios. This type of simulation provides management controllers with insights into the cost implications of alternative scheduling strategies. Similarly, [12] presented the Digital Supply Chain Twin paradigm, showing how simulation-based decision frameworks can improve resilience and efficiency. Although their study focused on supply chains, the underlying logic has strong parallels with hospital logistics, particularly in pharmaceuticals and equipment management, which are key cost centers. Costing and value measurement with AI support Accurate cost measurement is central to hospital management control. [13] showed that Time-Driven Activity-Based Costing (TDABC) in healthcare provides more precise cost allocations per patient pathway. They argue that AI tools can automate data collection on time and resource use, reducing the burden of manual cost tracking. By doing so, AI directly enhances the reliability of hospital cost accounting systems, which are often criticized for outdated or aggregated data. In parallel, [11] proposed the concept of value-based healthcare, where costs are systematically linked to patient outcomes. Integrating AI-enabled TDABC with performance measurement systems could strengthen hospitals’ ability to track value creation, aligning with the goals of management control. Barriers and limits to integration Despite these promising applications, several barriers remain. [17] identified low data quality, lack of interoperability, and resistance among staff as key factors hindering AI adoption in hospitals. [14] emphasized that organizational governance and culture play decisive roles in whether AI tools move beyond pilots to become embedded in decision-making processes. These limitations imply that, while AI can technically enhance MCS, its actual contribution depends heavily on governance, change management, and the alignment of incentives. Critical Review of the Literature (2020–2025) This section critically synthesizes empirical and conceptual contributions on AI in hospitals and draws explicit connections to management control systems (MCS). We structure the review around convergences, divergences, methodological limitations, and unresolved gaps relevant to budgeting, costing, and performance measurement. Convergences: What the field broadly agrees on (a) Predictive accuracy for patient flow is consistently higher with AI than with traditional baselines. Multiple studies report that machine learning models predict length of stay (LoS) and admissions more accurately than classical statistical methods, enabling better bed and staffing planning [8,15]. These results are repeatedly framed as operational enablers for planning and throughput, which are directly relevant inputs to capacity budgets and variance analysis in MCS. (b) Simulation and “digital twin” approaches help anticipate resource bottlenecks. Digital twins are described as useful to test alternative scheduling and configuration scenarios before implementation, reducing trial-and-error on the shop floor [16]. The same logic—using virtual replicas to evaluate throughput and resilience—appears in operations contexts and is transferable to hospital logistics [12]. For controllers, scenario evaluation informs ex-ante budgeting and sensitivity analysis. (c) TDABC provides more granular cost information than traditional allocations. In healthcare settings, Time-Driven Activity-Based Costing yields more precise, pathway-level cost attribution than department-level averages, and is explicitly reported to support managerial decisions [13]. This aligns with MCS objectives of improving standard cost setting and explaining budget variances. (d) Balanced Scorecard and integrated PMS improve accountability when financial and non-financial indicators are combined. Reviews of BSC in healthcare conclude that linking patient, process, learning, and financial perspectives enhances performance monitoring and accountability [2]. This convergence matters because AI outputs (predictions, alerts) can be embedded as leading indicators inside such systems. Divergences: Where findings or emphases do not align (a) From operational pilots to governance integration. While many articles show positive operational results (e.g., better LoS forecasts), several reviews highlight limited evidence that these tools are integrated into hospital governance and control cycles [5,14]. Some studies remain technology-centric; others emphasize organizational routines and culture. The divergence suggests a translation gap between proof-of-concept and routine MCS use. (b) Scope of impact measurement. Operational metrics (accuracy, waiting times, occupancy) are frequently reported, but financial impact (budget adherence, cost per case, variance reduction) is less consistently measured or causally established [5,15]. This creates disagreement about the magnitude of AI’s contribution to management control. (c) Data governance versus speed of deployment. Barriers such as data quality, interoperability, and explainability are well-documented [17], yet some implementations prioritize rapid deployment with limited model governance. Reviews that emphasize governance caution against scale-up without robust EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 221 data and model controls [14]. Thus, the literature diverges between “move fast” case reports and “govern first” frameworks. Discussion and Implications The conceptual model presented above highlights how AI can augment hospital management control systems (MCS) by providing predictive insights, granular costing, and simulation-based planning. This section discusses the implications of these findings for hospital managers and controllers, policymakers, and the academic community. Implications for hospital managers and controllers For hospital managers, the integration of AI into MCS provides a means to move from retrospective reporting toward real-time, predictive decision-making. [8] showed that machine learning models significantly improve length of stay forecasts, which can be directly translated into more accurate capacity budgets and staffing plans. When such predictions are embedded into budgeting processes, controllers can proactively manage variances instead of merely explaining them after the fact. In terms of costing,[13] demonstrated that Time-Driven Activity-Based Costing (TDABC) enhances cost accuracy by capturing resource use at the pathway level. When paired with AI for automated time data collection, this provides controllers with timely and precise cost information. This capability strengthens variance analysis and supports strategic investment decisions. Digital twin technologies offer additional benefits for managers by allowing them to simulate “what-if” scenarios before resource reallocation [16]. For example, adjusting the number of surgical slots can be tested virtually, with projected impacts on both waiting times and costs. Such tools transform the role of the controller from a passive reporter to an active partner in scenario-based decision-making. Implications for policymakers and healthcare governance At the policy level, the adoption of AI in hospital management control raises questions of data governance, accountability, and equity. [17] emphasized that low data quality and interoperability barriers remain major obstacles to AI deployment in hospitals. [14] further argued that governance structures—such as clear accountability for model outcomes—determine whether AI projects scale beyond pilots. Policymakers therefore need to establish regulatory frameworks and funding mechanisms that ensure hospitals can invest in robust data infrastructures and maintain transparency in AI-driven decision-making. Moreover, embedding AI into performance dashboards, such as Balanced Scorecards, may risk overemphasizing efficiency if not counterbalanced with equity and quality-of-care metrics [2]. Policymakers must thus encourage a balanced approach that integrates both financial control and patient-centered outcomes. Implications for academic research From an academic perspective, the proposed model underscores the need for empirical studies that trace full control loops—from prediction, to decision, to financial outcomes. [5] noted that most AI research in healthcare remains focused on clinical or operational outcomes rather than management control. Future studies should explicitly measure whether AI-enhanced predictions reduce budget variances, improve standard-vs-actual cost alignment, or enhance Balanced Scorecard performance. In addition, more research is needed on the moderating role of governance. While barriers to AI adoption are documented [17], few studies quantify how governance practices—such as model monitoring or data quality assurance—affect the financial benefits of AI integration. Multi-site comparative studies could clarify these dynamics and provide stronger causal evidence. Finally, the integration of TDABC and AI represents a promising but underexplored frontier. [13] demonstrated TDABC’s potential in healthcare, but empirical work linking AI-generated real-time data to TDABC-based cost systems is scarce. Exploring this linkage would directly address the needs of hospital controllers for accurate, timely cost information. Synthesis In sum, AI presents both opportunities and challenges for hospital management control. For managers, it offers tools to anticipate costs and optimize resources. For policymakers, it highlights the urgency of governance frameworks that safeguard quality and equity. For researchers, it opens a rich agenda focused on linking predictive analytics, costing, and performance measurement in integrated control cycles. The overarching implication is that AI does not replace management control—it transforms it by shifting it from retrospective monitoring toward proactive, simulation-based governance. Methodological limitations that weaken inference for MCS (a) Underpowered causal designs for budgetary outcomes. Many studies report improved predictions or simulated efficiency but lack quasi-experimental designs or multicenter comparisons that would isolate effects on budget variance, standard-vs-actual cost gaps, or BSC targets [15]. Without these, implications for MCS remain suggestive rather than demonstrated. (b) Short follow-up and narrow contexts. Evidence often comes from single hospitals or short time windows, limiting external validity for control cycles that operate annually or across networks [8,16]. That EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 222 weakens claims about sustained improvements to budgeting and performance dashboards. (c) Missing linkage between costing and predictive layers. Although TDABC improves cost granularity [13] and AI improves forecasts [8], few studies explicitly connect the two to show how forecasted volumes feed TDABC-driven standard costs or rolling budgets. This methodological separation hampers MCS integration. Substantive gaps and what they imply for management control Gap 1 — From predictive accuracy to budgetary value. The literature shows that AI can predict flows [8,15] but rarely tests whether using those predictions reduces budget variances or improves service-line profitability. Future work should test the pathway: predictions → staffing/bed decisions → cost variances within the MCS cycle [5]. Gap 2 — Governance as a moderator of performance effects. Barriers related to data and culture are welldocumented [14,17], but few studies quantify how governance practices (data catalogs, model monitoring, accountability) moderate the impact of AI on MCS outcomes. This invites explicit moderator analyses in multi-site settings. Gap 3 — Coupling TDABC with AI-enabled operational data. Evidence supports TDABC’s superiority for costing [13], yet studies seldom instrument TDABC with realtime timestamps generated by AI/IoT, and then track effects on standard-vs-actual cost gaps. Demonstrating this coupling would directly address controllers’ needs. Gap 4 — PMS and BSC as recipients of AI signals. BSC usage in hospitals is documented [2], but few papers describe how AI outputs become leading indicators inside PMS with clear target-setting, feedback loops, and accountability [6]. This limits translation into routine control practices. Gap 5 — Organizational design and budgeting routines. Classic work shows tensions between rigid budgets and dynamic care needs [1]. AI could mitigate these tensions via rolling forecasts, yet robust evidence connecting AI adoption to changes in budgeting routines is scarce. Implications for a control-oriented research agenda 1. Design studies that trace full control loops. Move beyond accuracy metrics to examine how predictions change decisions and how those decisions change budget variances and BSC targets [5,15]. 2. Test governance as a moderator. Compare sites with strong data/model governance against those without to quantify differential performance [14,17]. 3. Integrate TDABC with AI data streams. Instrument TDABC with automated time stamps and compare costing accuracy and standard cost adherence before/after [13]. 4. Embed AI signals into PMS/BSC. Specify AIderived leading indicators, target-setting rules, and accountability mechanisms within Ferreira & Otley’s PMS logic and hospital BSC practice [2,6]. 5. Consider organizational routine change. Examine whether AI adoption triggers shifts from fixed budgets to rolling forecasts or flexible variance thresholds in line with documented rigidity problems [1]. Conceptual Model: Integrating AI into Hospital Management Control Systems Building on existing frameworks The conceptual model proposed in this paper is designed by combining insights from established frameworks in management control and recent developments in healthcare AI: • Balanced Scorecard (BSC) [10]: widely applied in hospitals to link financial, patient, process, and learning outcomes [2]. • Time-Driven Activity-Based Costing (TDABC) [9]: shown to produce more accurate cost allocations per pathway and support managerial decision-making in healthcare [13]. • Performance Management System (PMS) framework [6]: emphasizes the importance of objectives, measures, targets, and feedback loops in aligning decision-making. • AI-enabled tools: including predictive analytics for patient flows [8,15] and digital twins for simulating and optimizing resource allocation [12,16]. Each of these frameworks addresses a specific dimension of hospital performance. However, current literature shows that they are rarely integrated. The model proposed here aims to combine them into a coherent architecture for AI-augmented hospital management control. Description of the proposed model The conceptual model (Figure 1, to be developed for the full article) links three core layers: 1. Data and prediction layer: o AI collects and processes data from electronic health records, enterprise resource planning systems, and operational databases. o Predictive models (e.g., LoS forecasting, demand forecasting) generate forward-looking insights that directly feed budgeting and planning cycles [8]. 2. Costing and measurement layer: o AI-enhanced TDABC tracks resource use in real time, enabling more granular costing per procedure or patient pathway [13]. o These costs are connected to BSC dimensions (financial, patient, internal processes, learning), ensuring a balanced performance perspective[2]. 3. Governance and decision layer: o Predictive indicators and cost measures are embedded in PMS feedback loops [6]. o Digital twin simulations allow managers to test “what-if” scenarios before implementing changes [16]. EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 223 o Governance structures ensure data quality, model accountability, and alignment with organizational strategy [14]. Propositions for future research From this conceptual model, we derive several testable propositions: • P1. Integrating AI-based LoS predictions into hospital capacity planning will reduce budget variances associated with unexpected admissions. • P2. The combination of AI-enabled TDABC and PMS frameworks will increase the accuracy of standard-vs-actual cost analysis, improving financial control. • P3. Digital twin simulations embedded in control systems will enhance the reliability of scenario planning, leading to more effective allocation of scarce resources. • P4. The impact of AI on hospital financial performance will be positively moderated by governance practices, such as data quality management and algorithm accountability. • P5. Hospitals that embed AI outputs into Balanced Scorecard dashboards will achieve higher alignment between operational decisions and strategic objectives. Contribution of the model This integrative model contributes to the literature by explicitly linking operational AI applications (prediction, optimization, simulation) with management control tools (budgeting, costing, PMS, BSC). While prior studies often examine these components separately, the proposed framework highlights their complementarity and suggests pathways for empirical validation. Limitations and Research Agenda Although research on artificial intelligence (AI) in healthcare has accelerated since 2020, the current body of evidence reveals significant limitations that restrict its translation into hospital management control systems (MCS). Recognizing these shortcomings is essential for designing a future research agenda that can strengthen both academic rigor and managerial relevance. Methodological limitations Overreliance on single-site studies. Many empirical studies rely on data from one hospital or health system, which constrains the generalizability of results.[8], for example, used a large dataset to improve length of stay (LoS) prediction, but their findings remain contextspecific. Similarly, [16] illustrated the potential of digital twins through conceptual modeling and simulation, yet without large-scale, real-world implementation evidence. Lack of causal designs for financial outcomes. [15] reviewed LoS prediction studies and found consistent improvements in accuracy. However, very few of these studies tested whether improved predictions led to measurable changes in financial performance, such as reduced budget variances or improved cost-tooutcome ratios. This methodological gap limits the ability to claim a causal link between AI adoption and MCS effectiveness. Fragmentation of domains. While TDABC has been validated as a costing method in healthcare [13], and predictive AI models have been validated for operational efficiency [8], the literature seldom integrates the two. Costing, prediction, and governance are often studied in silos, making it difficult to assess their combined effect on hospital control systems. Short-term focus. Most studies examine immediate improvements (e.g., prediction accuracy, shorter waiting times), but few analyze long-term sustainability of AI interventions within annual budget cycles or multi-year strategic control frameworks [5]. Theoretical limitations Limited integration with control system frameworks. Although the Balanced Scorecard [10] and Performance Management Systems [6] are widely used in management control research, AI applications are rarely theorized within these frameworks. Instead, they are often presented as technological add-ons rather than as integral components of hospital control systems. Insufficient focus on governance as a moderator. Barriers such as data quality and resistance to adoption are documented [14,17], but few studies measure how governance practices—such as data stewardship, algorithmic auditing, or accountability structures— affect the performance outcomes of AI adoption. Future research agenda Based on these limitations, we identify several priorities for future research. First, future studies should go beyond single-hospital case studies to include multi-hospital or multi-country designs, enabling stronger external validity. Quasi-experimental designs could test whether AI-enhanced predictions reduce budget variances or improve Balanced Scorecard indicators. Second, research should explicitly test the causal chain linking AI predictions to managerial decisions and then to financial outcomes. Without this, the impact of AI on MCS remains speculative. Third, scholars should combine TDABC with AI-driven operational data (e.g., timestamps, workload predictions) to test whether this integration improves standard-vs-actual cost analysis. Fourth, comparative studies should analyze how hospitals with strong governance practices (data quality management, model monitoring) achieve different outcomes than those without. Finally, longitudinal research is needed to assess the sustainability of AI interventions in annual budgeting and strategic EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 224 planning cycles, rather than short-term operational benefits alone. Synthesis In sum, the current literature demonstrates AI’s technical potential for prediction, optimization, and costing, but methodological and theoretical limitations prevent definitive claims about its impact on hospital management control. The next generation of research must therefore adopt designs that are broader, deeper, and longer-term, while embedding AI firmly into established control frameworks such as BSC, TDABC, and PMS. Conclusion This paper has critically examined the contribution of artificial intelligence (AI) to hospital management control systems (MCS). The review showed that AI provides clear opportunities to enhance forecasting of patient flows, optimize resource allocation, and improve costing accuracy through TDABC. However, despite these promising developments, current research remains fragmented, often limited to operational pilots, and rarely embedded into budgeting cycles or performance management frameworks such as the Balanced Scorecard. The conceptual model proposed in this article integrates AI tools with established management control frameworks, highlighting their complementarity. It positions AI not as a replacement but as an augmentation of management control, moving from retrospective reporting toward proactive and simulation-based governance. Importantly, the review identified several gaps, including the lack of causal evidence on financial outcomes, insufficient integration of costing and predictive tools, and the limited role given to governance as a moderator. The implications are threefold. For practitioners, AI offers controllers tools to anticipate costs and act as strategic partners in hospital decision-making. For policymakers, governance and data infrastructure remain critical prerequisites for successful adoption. For researchers, the agenda points toward multi-site, longitudinal, and theoretically grounded studies that explicitly connect AI interventions with financial and strategic control outcomes. Ultimately, hospitals will only capture the full potential of AI in management control if technological innovation is accompanied by robust governance, organizational learning, and integration into established performance frameworks. References [1] Aidemark LG. The meaning of balanced scorecards in the health care organisation. Financial Accountability & Management. 2001;17(1):23–40. doi:10.1111/1468-0408.00119. [2] Amer F, et al. The deployment of balanced scorecard in health care organizations: A systematic review. BMC Health Serv Res. 2022;22:148. doi:10.1186/s12913-022-07555-9. [3] Anthony RN. Planning and control systems: A framework for analysis. Boston: Division of Research, Graduate School of Business Administration, Harvard University; 1965. [4] Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188–e194. doi:10.7861/fhj.2021-0095. [5] Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94–98. doi:10.7861/futurehosp.6-2-94. [6] Ferreira A, Otley D. The design and use of performance management systems: An extended framework for analysis. Manag Account Res. 2009 Dec;20(4):263–282. doi:10.1016/j.mar.2009.07.003. [7] Hoozée S, Bruggeman W. Identifying operational improvements during the design process of a time-driven ABC system: The role of collective worker participation and leadership style. Manag Account Res. 2010 Sep;21(3):185–198. doi:10.1016/j.mar.2010.01.003. [8] Jain R, Singh M, Rao AR, Garg R. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res. 2024 Jul 30;24:860. doi:10.1186/s12913-024-11238-y. [9] Kaplan RS, Anderson SR. Time-driven activitybased costing: A simpler and more powerful path to higher profits. Boston: Harvard Business Press; 2007. [10] Kaplan RS, Norton DP. Strategy maps: Converting intangible assets into tangible outcomes. Boston: Harvard Business School Press; 2004. [11] Kaplan RS, Porter ME. How to solve the cost crisis in health care. Harv Bus Rev. 2011 Sep;89(9):46– 52. [12] Longo F, Padovano A, Umbrello S. The digital supply chain twin paradigm for enhancing resilience and sustainability. Comput Ind. 2023;149:103913. doi:10.1016/j.compind.2022.103913. [13] Niñerola A, Hernández-Lara AB, SánchezRebull MV. Improving healthcare performance through Activity-Based Costing and Time-Driven Activity-Based Costing. Int J Health Plann Manage. 2021 Nov;36(6):2079–2093. doi:10.1002/hpm.2999. [14] Roppelt JS, Kanbach DK, Kraus S. Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors. Technol Soc. 2024;76:102387. doi:10.1016/j.techsoc.2024.102387. [15] Stone K, et al. A systematic review of the prediction of hospital length of stay. J Biomed Inform. 2022 Jul;130:104087. doi:10.1016/j.jbi.2022.104087. [16] Vallée A. Digital twin for healthcare systems. Front Digit Health. 2023;5:1253050. doi:10.3389/fdgth.2023.1253050. EUR J MED HEALTH RES Volume 3 | Number 3 | May-June 2025 225 [17] Ahmed MI, et al. A systematic review of the barriers to the implementation of AI in healthcare. Cureus. 2023 May 15;15(5):e44324. doi:10.7759/cureus.44324. [18] Topol EJ. Deep medicine: How artificial intelligence can make healthcare human again. New York: Basic Books; 2019. [19] Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020 Jul 15;8(7):e18599. doi:10.2196/18599. [20] Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016 Oct 13;375(15):1216– 1219. doi:10.1056/NEJMp1606181. [21] van de Wetering R, et al. The impact of hospital information systems on management control: a systematic review. Health Policy Technol. 2021 Jun;10(2):100517. doi:10.1016/j.hlpt.2021.100517. [22] Hamami F, Jouini O. Artificial intelligence in healthcare operations: A review. Int J Prod Res. 2020 Sep;58(18):5742–5764. doi:10.1080/00207543.2020.1780531. [23] Alami H, et al. Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation. CMAJ. 2020 Nov 9;192(44):E1254–E1258. doi:10.1503/cmaj.200978. [24] Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019 Apr 4;380(14):1347–1358. doi:10.1056/NEJMp1903100. [25] Elbanna S, Child J. The influence of governance, strategy and performance management on hospital innovation. Public Manag Rev. 2021 Jul;23(7):1001–1025. doi:10.1080/14719037.2020.1744365. [26] Silva E, et al. Artificial intelligence in healthcare: Barriers and facilitators for implementation. BMJ Health Care Inform. 2022 Jan 6;29(1):e100495. doi:10.1136/bmjhci-2021-100495. [27] Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research. 2018;81:1–15. [28] Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2021 Mar 10;19:57. doi:10.1186/s12916-021-01994-7. [29] Ivanov D, Dolgui A. A digital supply chain twin for managing disruptions. Int J Prod Res. 2023 Jun;61(11):3552–3567. doi:10.1080/00207543.2023.2190143. [30] Williamson B, Eynon R. Historical threads, missing links, and future directions in AI in education. Learn Media Technol. 2020 Jul;45(3):223–235. doi:10.1080/17439884.2020.1776889. [31] Nassou Y. Public Health and Economic Resilience in Morocco: Towards a Sustainable Healthcare Model. Int J Sci Res Innov Stud. 2025;4(1):67–77. doi: 10.63883/ijsrisjournal.v4i1.460