The Evolution of Institutional Logics in Medicine: Current Trends and Prospects for Resolution

Healthcare is shaped by competing institutional logics—professional, managerial and market—each influencing medical practices and business models. These logics often clash, especially between quality care and cost-efficiency. AI holds the potential to ease these tensions by streamlining workflows and freeing up resources, particularly through non-interpretative applications in administration and operations.

Key Point

• Multiple institutional logics shape modern medical practice and healthcare strategies.
• Conflicts often arise between professional, managerial and market-driven healthcare models.
• These tensions impact value propositions and the structure of healthcare business models.
• AI offers tools to optimise workflows and reduce resource strain in clinical and administrative tasks.
• Non-interpretative AI solutions may have the most measurable impact on healthcare delivery.

Institutional Logics

Institutional logics can be defined as a set of practices and beliefs that predominate in an organisational or professional field (Scott 2000). Generally speaking, an organisational field is governed by a dominant logic that guides practices and strategies within a given profession (Thornton et al. 1999). In complex organisational fields, several logics may coexist with a codominance effect (Dunn et al. 2010).

When several logics coexist, conflicts may arise, with several possible responses. The first response is a relationship of domination, where one logic dominates the others and guides practices within the profession. The second response is one of a power struggle over a long period of time, with no single logic standing out. This response may lead to a relationship of dominance by one of the logics or to a status quo. The third response is characterised by multiple competing logics with a codominance effect that results in heterogeneity of practices within the profession or an organisational field (Goodrick et al. 2011).

Institutional Logics in Healthcare

Like other sectors and professions, healthcare has evolved significantly in recent years. The paradigm shift in the doctor-patient relationship, the digital transformation of healthcare, access to information and the arrival of AI have profoundly altered the medical sector and the relationship with patients. In the past, the doctor-patient relationship was based on paternalism, where the doctor was seen as all-powerful. Respectful of medical power, patients followed medical instructions without reservation. Nowadays, with access to information via the Internet, the free choice of doctors and the diversity of services available, the relationship between patients and doctors is changing. This paradigm shift is linked to changes in institutional logic within the medical sector.

Since the last century, medicine has seen the emergence of multiple institutional logics that have changed medical practices and strategies within the profession. According to Scott (2000), whose work is based on the American healthcare system, medicine has been marked by three distinct periods (Figure 1). Each period was governed by a dominant institutional logic.

The first period, from 1945 to 1965, was governed by a professional logic. Medical practices were centred on competence and medical expertise, of which doctors were the guarantors. This period was based on medical paternalism. The terms of payment between patient and doctor minimised the intervention of third-party payers, giving doctors a high degree of autonomy. The second period, from 1966 to 1982, saw the arrival of the state as a new player in the healthcare sector. Access to healthcare was seen as a right for all, with the introduction of numerous health programmes. From the 1970s onwards, the costs of these services rose rapidly, necessitating more drastic control and better management of healthcare costs. This period was governed by a managerial logic in which cost control and better resource management were major objectives.

The third period runs from 1983 to the present day. This period saw the emergence of economists as new players in the healthcare sector. During this era, state regulation of the healthcare system was challenged by economists, who turned to a system governed by competitive mechanisms. This change in institutional logic towards the market had a significant impact on doctors, who went from being self-employed to salaried employees. It was also during this period that the concept of patient-centred medicine emerged. This approach focuses on the communication of care to patients, health promotion and the partnership between care providers and the patient (Constand et al. 2014)

Conflicting Logics in Healthcare

As discussed in the previous chapter, modern medicine is governed by a market logic based on competition and patient-centred care. Collaboration with the patient is privileged over the paternalistic relationship. Despite a dominant market logic, conflicts exist with other institutional logics within the healthcare sector. The conflict most frequently encountered in the healthcare sector concerns the professional logic of healthcare professionals, who are confronted with the managerial logic of administrators. In this conflict, professional logic is gradually abandoned in favour of managerial logic (Power 1999). Another response may also arise from this struggle. Co-optation is an adaptation by professionals that consists of adopting an element of another logic while retaining the main elements of the dominant logic. And finally, decoupling is a possible response to this conflict. In decoupling, care professionals only partially adopt practices derived from a managerial logic. The practices are only applied in a partial, ritualistic way, with no impact on actual work. The impacts are only minor (Andersson et al. 2018).

The Impact of Conflict on the Medical Business Model

As with any business that needs to generate profits, medical centres or hospitals rely on a business model to operate and generate earnings. Although there is no consensus on the definition of a business model, it can be defined as a set of functions that includes value proposition, target market, revenue generation and strategy (Chesbrough 2007).

The concept of the “business model canvas” is proposed by Osterwalder et al. (2010). The business model canvas is a visual tool containing nine blocks representing a key business function. The value proposition is one of these nine elements. The value proposition is seen as a benefit provided to customers in return for a cost (Barnes et al. 2009).

The value proposition in healthcare differs according to the point of view adopted. For doctors, the value proposition is based on evidence or “evidence-based medicine”. For managers, the value proposition is defined by the health benefit obtained in return for a cost (Marzorati et al. 2017). In his study of institutional logics and the value propositions of a radiology business model, Vo (2024) demonstrated a complex relationship between the value propositions arising from different institutional logics. According to Vo (2024), the different value propositions used in the radiology sector stem from different institutional logics, which include professional, market and managerial logics. This principle can be extended to the broader healthcare sector. Thus, the value propositions derived from a radiology business model are a heterogeneous set of values.

In the medical imaging sector, Vo has identified several types of conflict, all of which have elements of managerial logic as their common denominator. The first type of conflict (Figure 2) concerns value propositions derived from a professional logic that are at odds with elements derived from a managerial logic. This situation is a caricature of today's world. In this case, managers are allocating fewer and fewer resources to healthcare professionals, who must always do more with less. The result is a decline in the quality of care, to the detriment of productivity.

The second conflict (Figure 3) is more prevalent in the medical imaging sector, where sub-specialties are given greater prominence and represent quality medicine according to professional logic. Value propositions linked to overspecialisation in medicine or radiology clash with market value propositions whose values are based on comprehensive patient care and broad services in order to recruit a greater number of patients.

As demonstrated by Vo (2024), centres that focus on highly specialised services are unable to offer a wide range of services for organisational and economic reasons. The costs of offering overspecialised services are very high. On the other hand, health institutes offering more “generalist” care have more opportunities to recruit a larger number of patients. When a patient requires more specialised expertise, he or she is referred to these specialised centres. Moreover, an organ- or subspecialty-based approach does not allow for holistic patient care.

The Role of AI in Managing Conflicting Institutional Logics

Over the past decade, advances in computer science have been meteoric, with the development of Deep learning, Machine learning and Big Data. The advent of artificial intelligence (AI) has opened up new perspectives in medicine, particularly in the interpretative solutions which are common in radiology. Today, there are many applications in medicine (Figure 4). The market offers applications for remote patient monitoring, sensor-based monitoring of chronic pathologies and infection diagnosis. AI solutions involve patients directly in their care, offering applications that facilitate their treatment. Solutions are used in rehabilitation programmes, notably in perioperative medicine or in the execution of fitness exercises.

Aside from all these medical-based AI solutions, other applications (Figure 5) optimise workflow and simplify administrative tasks (Al Kuwaiti et al. 2023). In the radiology sector, such AI solutions can automate examination protocols (Brown & Marotta 2017), prioritise radiological examinations according to degree of urgency (Richardson 2021), generate summaries from information provided by corresponding physicians (Rush et al. 2015), standardise radiology reports, plan radiology appointments according to cancellations and examination duration (Lakhani et al. 2018), inform patients in real time of waiting times (Thompson et al. 2024), manage staff and work schedules (Saini 2023) and automatically bill examinations or services performed (Denck et al. 2019).

As discussed above, most of the conflicts generated have as their common denominators value propositions derived from a managerial logic that aims to rationalise resources. Thus, from a hypothetical point of view, AI solutions would make it possible to free ourselves from certain resources by automating processes. The time saved would then allow more time to be devoted to tasks with real added value for the patient. The example of voice recognition in radiology is an example of the added value of AI. AI, particularly through machine learning, has enabled radiologists to directly dictate their reports, without having to go through the manual data entry phase previously carried out by secretaries. Freed from this non-value-added task, secretaries now have more time to admit and manage patients.

Conclusion

Almost imperceptibly, our practices and strategies in medicine are shaped by various value propositions stemming from different institutional logics. These logics govern the medical sector and are in constant competition. As a result, value propositions can come into conflict within a single business model.

The main tensions often arise from elements of a managerial logic aimed at rationalising costs. The implementation of AI could theoretically help overcome these conflicts. AI solutions could make it possible to bypass the limitations related to resources. Currently, most AI research focuses on interpretative solutions. However, from an operational and managerial standpoint, non-interpretative solutions—such as workflow optimisation and management applications—would likely have a more significant impact on the work of healthcare professionals. These impacts would also be easier to quantify. Therefore, future research should prioritise this direction.

Conflict of Interest

None