Within-case analyses
Table 2 summarizes the departments’ contextual characteristics as voiced by the interviewees to explain their department’s position vis-a-vis the implementation process including their reasons for the departmental adoption intention. The final column specifies the realized Plateau 1 adoption. In the next section, based on a cross-case analysis of the resulting patterns, the individual departments will be grouped into three types. To help interpret the chain of evidence leading to this typology, Table 2 already includes each department’s type. For each of the three types, this section further presents one representative department: Department A represents organization-oriented departments (Type I); C those with an internal orientation, i.e. a department-oriented department (Type II); and G an external environment-oriented department (Type III). Descriptions of the other departments are included in Additional file 3.
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Example of an organization-oriented department
Pre-implementation. Department A is a generic small subunit staffed by six medical staff members and one psychologist. The department is responsible for a small outpatient clinic (with three nurses). There is already a high degree of digitalization of patient records. Exceptionally in this hospital, the nurses at the outpatient clinic were already working with digitalized records. The interviewees emphasized their multidisciplinary patient focus, expressed by one of the doctors as follows: “We are one of the very few departments that focus on our patients and not so much on diseases” (A1). The department does not have its own nursing ward so patients who need hospitalization are admitted to wards belonging to various other departments. The clinicians from Department A will visit their patients in these wards. As such, their working practices are highly reliant on cooperation with other departments. Moreover, data are exchanged with care providers outside the hospital, particularly when patients are referred to the hospital by these providers and vice versa. Perhaps as a consequence, the interviewees welcomed the idea of a hospital-wide EHR and expressed no doubts about adopting the EHR as a department. While they voiced some concerns about whether it would match their own distinctive, holistic logic of patient record keeping, they saw no active role for their department. The interviewees admitted that given the department’s small size, and other developments requiring their attention, the new EHR was not their key priority. They thus seemed to be opting for a wait-and-see role. Nevertheless, the department clearly intended to comply, and indicated that their operations would benefit from a single shared patient record across departments and across professions. In short, they did not shy away from digitalization: “We are a forerunner in working in a digital way” (A2).
Realized adoption. Although the department’s members were slightly concerned that their specific multidisciplinary approach may be in jeopardy because of the system, they complied with adopting the system as implemented in Plateau 1 without demanding customized extras. The two interviewed users were happy with the progress in digitalizing patient-related data, as noted by one of them “…we catch up now, and I’m happy with it [the EHR]” (A4)
Example of a department-oriented department
Pre-implementation. Department C is a medium-sized mono-disciplinary subunit, consisting of doctors (14 specialists and 13 residents), nurses, paramedics, and technical support staff (approximately 120 fte in total). Although the department provides consultations for other departments’ patients, it works largely in a stand-alone manner. The department’s key activities are related to service delivery within a large outpatient clinic. They use advanced equipment that is fully integrated into the department’s care processes. They treat approximately 300 patients per day and for each there is a strict and limited timeslot available. The care processes are organized in such a way that the patients, depending on their diseases, complete a number of steps of this process. In the past, Department C had put great effort into designing efficient work processes. Given their existing tailored care processes, they only envisaged minor benefits from the new EHR. The department’s manager expressed this as: “it can save some time in routing the patient records from the depot through the process…but we are particularly concerned that the system is able to follow a patient through our process, that is how we have organized it logistically” (C3). As such, the department was worried that the new EHR might create more problems than benefits. During the pre-implementation phase, it was uncertain whether the new EHR could be aligned with the department’s existing work processes that were integrated with the technological equipment in use.
Realized adoption. Plateau 1 was implemented without any customized extras for Department C. Due to the high volume of patients each day, it was crucial that the department could quickly retrieve concise specialty-related medical histories of their patients. The system could not meet this requirement. Therefore, the department’s board decided to create a workaround, which all its specialists were obliged to use: “…it is not even allowed to use it in a different way; these are firm arrangements within our department” (C4). They acknowledge that their view conflicts with having a unified organization-wide EHR: “Our way of working goes against agreements” (C5).
Example of an external environment-oriented department
Pre-implementation. Department G is a large subunit (230fte) that has its own ward and outpatient clinic. Here, new patients are filed digitally, while existing patients still have paper-based records (internal documents). This department is not located in the main building and their care processes have little work dependencies with other departments. One manager explained: “Some departments operate in a very isolated way within the organization. We are very isolated… our information systems differ considerably from the other information systems…we don’t collaborate that much” (G2). Nevertheless, they showed themselves to be highly committed to helping the change take place and the interviewees did see benefits in the new system for their department: “People are intending to use the new system…they can hardly wait, because we are faced with the limitations of [the current information system]” (G2). They also saw it as a way to become better connected to the other departments. The interviewees did not worry about task-technology fit: “…business as usual, with another information system” (G2) and “this is about finance and bureaucracy for the most part, the way we provide care will not change that much” (G1). Despite the seemingly enthusiastic support, the department board’s intention to adopt was clearly conditional: “We really want one [generic] information system. We are true proponents! However, it has to go our way...we will still have to comply with our own [external] rules and legislation” (G4). It was uncertain whether the new system would facilitate these requirements.
Realized adoption. Despite the department’s supportive attitude towards implementing the EHR, the EHR proved incapable of supporting the production registration system demanded by the insurers. Implementation was, therefore, suspended until further notice.
Cross-case analysis
The descriptions of Departments A, C, and G show different emphases in their work context orientations: towards the hospital (A), towards the department itself (C), and towards the external environment (G). These work-context-based orientations not only play a role in the adoption intentions, but are similarly reflected in the Plateau 1 realized adoption: Department A achieves a rather unproblematic adoption, in Department C we observe the collective creation of workarounds and, in Department G, the EHR was not adopted. In this section, we present the cross-case analysis leading to these distinct types.
Context characteristics and related adoption
The context characteristics that interviewees used to explain their department’s adoption intention are provided in the second column of Table 3. Based on our interpretation of the shared elements in their narratives, as summarized in the within-case summaries above and in Additional file 3, the final column specifies the direction (positive +; negative -; or ambivalent +/-) and dominance (in bold) of the characteristic’s influence. The table shows that the dominant characteristics relate especially to work context and less so to the socio-political context.
First, the accounts from five departments point to the relevance of a fit between the new technology and the existing material and human technical capabilities. In three departments (D, F, G), the existing IT expertise seemed to positively influence the adoption intention while, in two other departments (C, E), the local IT expertise, as well as the existing or envisioned technology, had a negative effect. Department E had started to independently develop its own IT: “At E we now have a patient management data system […] which we will keep using. Thus, we will not fully adopt the EHR” (E3). They expressed strong doubts as to whether this system could even be connected to the organization-wide EHR. The key actors in Department C voiced similar concerns about the possible integration of, or interfacing with, the numerous and advanced technologies employed in their efficiently organized work processes, leading to strong reservations towards EHR adoption. In terms of the material technical capabilities, two departments (B and G) explained that they expected better connections or integration of their systems.
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Second, the work context points to a perceived fit between the EHR and the departments’ tasks and current work designs. The table’s third column presents a range of characteristics related to task-technology fit that were voiced during the interviews. Clearly, perceiving the existing administration as inefficient, unreliable, or fragmented is a driving force for EHR adoption, and this perception was mentioned by all departments except Department C. Expecting current practices to benefit from the uniformity that the new EHR system imposes was also a positive stimulus for intended adoption (D, E, and F). Department C demonstrated how having a well-designed, but vulnerable to change, workflow makes departments suspicious of a new system that will cause disruption.
Third, the interviews exposed how a department’s environment plays two counteracting roles determined by (1) a department’s dependencies on other departments within the hospital (intra-organizational) and (2) its dependencies on the hospital’s environment (extra-organizational). For six departments, their dependencies on other departments can be seen as a major reason for their intended adoption: they deliver either multidisciplinary care or share patients with other departments. These departments expect greater benefits from a hospital-wide EHR system than departments that operate in a relatively ‘stand-alone’ fashion, such as Departments C and G. When it comes to extra-organizational dependencies, the picture is less straightforward. Interviewees from four departments voiced the relevance of extra-organizational dependencies (B, D, F, and G), with those from D and F perceiving opportunities to improve their dealings with supply chain partners or in complying with legal requirements. However, Department B was highly ambivalent regarding the system’s potential to facilitate external information flows. Moreover, Departments B and G did not see how this new EHR could possibly support them in meeting externally imposed information requirements.
Regarding the socio-political context, the interviewees’ narratives did not reveal any dominant role of these characteristics: as Table 3 indicates, our findings provided no indications of a direct relationship between these characteristics and a departments’ adoption intentions.
Pattern identification: three department types
By examining the interplay between the contextual characteristics and then relating the identified patterns to the department’s adoption intention, we were able to determine the dominant factor(s) for each department (shown in Table 3 by the department letters in bold). For example, Table 3 shows that the material technical capabilities of Departments C and E, critical resources for delivering their specialty’s patient care, had the most impact on their adoption intention. Although these departments were supportive of the change direction, they had developed conditional, or limited, adoption intentions (Table 4; see also Table 2). For these two departments, their existing, highly advanced, technical capabilities had a negative influence on their adoption intention. The representatives voiced this aspect as dominating their ultimate adoption intention: “[The existing technologies] are an incredibly huge investment that [Department C] has made, so imagine that you have just adopted these and then they get thrown out once the EHR is implemented, then you end up totally suckered”. For these departments, this aspect was perceived as more critical than other influences (such as department E’s references to ‘IT expertise and vision for the future’).
In contrast, the low technical capabilities of Departments B and G had a positive influence on their adoption intentions. Department B’s interviewees mentioned that greater integration between applications and a long-desired connection with a specific technology were expected to be realized through the EHR implementation. However, their serious concern was that this positive influence would be outweighed by the negative effect of this department’s dominant factor: its extra-organizational dependencies related to national laws and specialty regulations, and the integrated workflows with external partners: “We have many referrals from a large region, and the doctors from our specialty are also based in almost all the hospitals in the region” (B1). Despite the espoused support for the organization-wide EHR implementation, these characteristics led to reservations about their own adoption in the pre-implementation stage.
The pattern identification process resulted in three department types that varied in their adoption intentions: (I) organization oriented, (II) department oriented, and (III) external environment oriented (Table 4). The first type with an organization orientation (A, D, F, and H) had many intra-organizational dependencies. These were in the form of workflow dependencies with other departments because they deliver multidisciplinary care and/or treat patients from other departments. Such departments are relatively more dependent on inter-departmental information flows than are other departments, and A, D, F and H were clearly dissatisfied with their current systems. Their expectation was that the EHR would enable high quality information exchange with other departments, and voiced this as the dominant reason for their intended adoption of the system. In these departments, the data show relatively unproblematic realized adoption, although the efficiency of the use was sometimes a little lower [estimation of program management for D and H].
The second type had a department orientation (Departments C and E) and characterized themselves as having relatively stand-alone work processes with few reciprocal dependencies with other departments. These departments had serious doubts as to whether their current technologies-in-use could be safeguarded or further developed under the new standardized systems and therefore only intended to adopt the EHR in a limited or selective way. Both departments were rather satisfied with their existing technologies (some under development), and their relative independence or even isolation had allowed them to carefully integrate these into their well-designed work processes. This strengthened their reservations. In the end, these departments had nevertheless to implement Plateau 1. In Department C, a collective workaround was determined to secure their own throughput efficiency until a customized module was provided. In Department E, many of the analytical functionalities of their highly customized patient data management system were lost. This was, however, partly compensated for by a medical specialist who managed to acquire high EHR expertise and so build customized flowcharts for their clinical processes and specific order sets, and also generate the required data analyses “deep within” the EHR itself. Finally, halfway through 2018, both departments were provided with a ‘physician builder’ – a physician responsible for maintaining and updating their own EHR configuration.
Representing the third type, with an external environment orientation, were Departments B and G that had significant extra-organizational dependencies through regional or (inter-)national collaborations, or through laws and insurance regulations. These departments were highly supportive of implementing an EHR, but said they could only adopt the new system to the extent that it would not hinder them in fulfilling their external requirements. Their fears in this respect showed in pre-implementation reservations. The post implementation data show that it was negotiated that Department G, facing externally imposed production registration requirements that went against the EHR logic, did not adopt the system. A customized module was developed for Department B to enable it to meet its external demands. Moreover, a third system that the physicians worked with, alongside the two applications replaced by the EHR, would be integrated in the EHR for Department B. The latter customization was, however, ultimately postponed to Plateau 2 by the supplier. Consequently, both a medical specialist and the project manager acknowledged that the outcome was perceived as complicated to work with, leading to individual physicians using workarounds.
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