Navigation towards excellence or distraction from the real deal?
A study on how Electronic Health Records Inform Organizational Learning
Master Thesis
MSc Change Management Faculty of Economics and Business
University of Groningen
Student & student number: Alies Meijer (S2121190) Supervisor: Dr. N. Renting
Second assessor: Dr. M.A.G. van Offenbeek Date: 21-6-2019
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Abstract
Context.
Although the idea of organizational learning (OL) supporting IT adoption is applied to electronic health records (EHRs) in a considerable number of studies, the reversed relationship in which EHR implementation possibly informs OL remains undertheorized. This seems surprising given the central function of information and decision support in EHRs, which implies potential for learning. A scarce amount of studies suggests technical potential of the EHR as a tool for education of individual residents. However, insights into the broader impact of EHRs on learning are missing.Objectives.
The aim of this study is to move beyond the technical- and individual level focus of existing literature by exploring how EHRs inform OL.Methods.
We conducted case studies in internal medicine departments of three hospitals with different EHR systems. Interviews, observations, and secondary data formed the input for iterative data collection and analysis. Informed grounded theory guided this process and supported rigor and emerging insights. Activity theory served as a sensitizing framework for data analysis, which allowed us to theorize the impact of EHRs on OL in the context of broader organizational elements in addition to technical elements.Results.
EHRs positively influenced knowledge distribution through improved information access, and facilitated organizational level knowledge interpretation by transforming information into medical knowledge. However, knowledge acquisition and individual knowledge interpretation outcomes were less promising. EHRs decreased patient contact, which hampered learning by doing and reduced opportunities to develop patient-centered competences. Furthermore, standardization and intelligent functionalities in EHRs seemed to reduce clinical reasoning competences and trigger a shift from human- towards system memory. EHR design and use behavior showed to be highly relevant in the emergence of such mechanisms.Conclusions.
EHRs offer great learning potential, but realization of this potential falls behind. The system can be a useful tool for distribution of new knowledge and insights across the organization. However, hospitals should be careful not to dwell in an overkill of standardized forms and intelligent functionalities in order to retain the qualities of their human capital and to achieve the desired shift towards better patient-oriented competences.2
1. Introduction
The explosion of knowledge and short cycles of technological innovation form important drivers of today’s turbulent healthcare environment (Carroll & Edmondson, 2002; Layman & Bamberg, 2006). Both of these drivers strongly relate to the recent upswing of Electronic Health Records (EHRs) (Boonstra, Versluis & Vos, 2014). Organizational learning (OL) became a key tool for coping with turbulence (Walston, 2016) and offers a fundamental source of responsiveness and competitive advantage through continuous improvement (Odor, 2018). Literature describes a two-fold relationship between OL and technology in general, where OL supports adoption of technological changes as well as the other way around (Robey, Boudreau & Rose, 2000). Although the idea of OL supporting IT adoption is applied to EHRs in a considerable number of studies (e.g. Robinson & Kersey, 2018; Stevens et al., 2017; Takian, Sheikh & Barber, 2014; Tucker, Nembhard & Edmondson, 2007; Wu, Wang, Song & Byrd, 2015), the reversed relationship in which EHR implementation possibly informs OL remains largely overlooked. This seems surprising, given the central function of information and decision support (Menachemi & Collum, 2011) in EHRs, which implies potential for learning. This study brings two challenges of healthcare organizations together by examining how EHRs inform OL.
EHR is often considered as a generic term for computer processable repositories of health status related information of a subject of care (Boonstra et al., 2014). These repositories should enable instant information availability (Boonstra & Broekhuis, 2010) and provide structure in the highly unstructured healthcare environment. A recent poll (Stanford Medicine, 2018) indicates that the majority of healthcare professionals sees the EHR as digital information storage, rather than a clinical tool. Thus, the question remains to what extent EHRs are merely digitalized paper files or that their function expands to benefits such as learning. OL entails a learning process that involves the interaction of individual- and collective levels of learning within organizations and contributes to achieving organizational goals (Popova-Nowak & Cseh, 2015).
Potential mechanisms through which EHRs inform OL remained undertheorized, but recent studies on individual resident education (Atwater et al., 2016; Habboush, Hoyt & Beidas, 2018) and machine learning (e.g. Anderson et al., 2015; Hu et al., 2017; Rubio-Lopez et al., 2017; Xiao, Choi & Sun, 2018) do emphasize EHR’s potential for learning in general. While these studies provide insights in EHR’s potential for individual education, additional insights are needed to draw conclusions on EHR’s potential for OL (Crossan, Lane & White, 1999). OL entails more than just a ‘collectivity of individual learning processes’, since it entails the interaction between individual- and organizational levels of learning (Wang & Ahmed, 2003, p.15). This study aims to build on- and go beyond existing individual level studies to explore mechanisms through which EHRs influence OL. The research question central to this study is: ‘How do Electronic Health Records inform Organizational Learning?’. Through qualitative research we explore these mechanisms with the use of informed grounded theory as a methodological foundation for data collection and -analysis.
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how information is used (Bharadwaj, Sawy, Pavlou & Venkatraman, 2013) and how knowledge sharing and integration are facilitated (Kale, Dyer & Singh, 2002). This study provides insight in these mechanisms and gives concrete recommendations for practitioners to enhance value of EHRs for OL.
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2. Literature review
This chapter provides an overview of relevant literature for examining mechanisms through which EHRs inform OL. First, the function and context of EHR systems (2.1) and OL frameworks (2.2) are explored. Literature on the relationship of EHRs with both individual learning (2.3) and OL (2.4) is discussed afterwards. Although this study focuses on interactive learning mechanisms related to OL, literature on individual learning is included since OL is about learning in the interactions between these individuals. In line with the constructivist paradigm (see Chapter 3), the concepts explored in this chapter serve as a sensitizing tool rather than an explicit framework for guiding the qualitative research (Charmaz, 2006).
2.1.Electronic Health Records
The introduction of EHRs is one of the most impactful changes in healthcare (Heath & Porter, 2018). Potential benefits of EHRs include reduced costs and enhanced patient safety, quality and continuity of care (Chao, 2016; Hillestad et al., 2005; King, Patel, Jamoom & Furuwaka, 2014; Nguyen, Bellucci & Nguyen, 2014). These benefits should result from improved communication, coordination, rapid learning and interdisciplinary collaboration (Chao, 2016; Zeng et al., 2011). However, outcomes of EHR implementation often do not meet these expectations (Chao, 2016; Nguyen et al., 2014) and can have unanticipated consequences such as loss of communication, workarounds, and changes in power structure (Harrison, Koppel & Bar-Lev, 2007; Menachemi & Collum, 2011). A current challenge is that EHRs “do not meet the needs of today’s rapidly changing healthcare environment” (Evans, 2016, p.54). OL is a key tool for coping with this turbulent environment (Walston, 2016), which makes it increasingly interesting to examine the impact of EHRs on these OL processes.
EHR systems can differ in architecture as well as use contexts, and adoption levels tend to fluctuate across different hospitals (Palabindala, Pamarthy & Jonnalagadda, 2016). This calls for a careful approach to generalization of empirical results. EHRs can include different functionalities and applications (Montero & Prado, 2009), which influences search effectiveness and knowledge diffusion capabilities (Zheng, Mei & Hanauer, 2011). Furthermore, use contexts range from hospitals to pharmacies and other healthcare providers (Boonstra, Boddy & Bell, 2008). Hospitals are particularly challenging due to complexity of medical data, confidentiality concerns, multiple objectives, and highly diversified structures (Boonstra & Goovers, 2009; Grimson, Grimson & Hasselbring, 2000).
2.2.Organizational Learning
Organizational learning (OL) focuses on the process through which knowledge is acquired and distributed (Easterby-Smith & Lyles, 2003). In the context of this study, these OL processes entail changes in knowledge and skills of individual healthcare professionals, the hospital as a whole, and the interactions between those. The relationship between individual- and organizational learning has been prone to extensive debates. In this study, I join Wang & Ahmed’s (2003) view of OL as comprising interactional processes between individual- and organizational levels of learning. Hospitals learn through their individual employees and therefore individual learning is part of OL. However, OL goes beyond individual learning by including processes through which generated knowledge can be saved, retrieved and re-used by organizational actors. These processes transform the knowledge and skills possessed by individuals into collective organizational assets. OL is especially relevant in healthcare, since failures often result from a lack of interpersonal or intergroup learning, and considerable interdependencies create potentially disastrous situations unless learning takes place (Walston, 2016). In addition, activity across departments is difficult due to absence of overseeing managers (Tucker & Edmondson, 2003).
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learning. Huber’s (1991) framework is generally considered as a solid framework in the context of OL and information systems (Batista & Figueiredo, 1999) and distinguishes four constructs in OL. Knowledge acquisition (1) is the process of acquiring information or knowledge through different types of learning and offers the potential for information distribution (2): sharing knowledge with other organizational components. Distribution determines the extent to which OL takes place, but potential combination of information can only be exploited when the information is actually interpreted (3) by giving meaning to the information (Daft & Weick, 1984). To assure sustainability of valuable knowledge, it is incorporated in organizational memory (4) situated in the minds of employees or in information systems, procedures, routines and scripts. In the context of EHRs, the question remains what the consequences of EHRs for these knowledge transfer processes are. EHRs might positively inform OL when they facilitate distribution of acquired knowledge, when distributed information is interpreted in meaningful ways, and when the system serves as a useful organizational memory depository.
While Huber’s framework focuses on translation of information to organizational memory, Nonaka & Takeuchi (1995) focus explicitly on the knowledge distribution part of this process. They distinguish four knowledge transfer processes that relate to different types of knowledge. Explicit knowledge can be codified and communicated through symbols or language (Nonaka, 1991), while tacit knowledge contains technical skills and mental models acquired in informal learning which are difficult to transfer through written communication (Dampney, Busch & Richards, 2007). Socialization entails sharing tacit knowledge through direct experience, which is limited in its ability to spur organizational knowledge creation, since knowledge does not become explicit (Basten & Haamann, 2018). In contrast, externalization refers to articulating tacit knowledge to explicit knowledge through reflection and dialogue (Nonaka & Konno, 1998). Combination is the sharing of this explicit knowledge by systemizing and applying it, making the combination of a group’s internal knowledge with external knowledge possible (Basten & Haamann, 2018). Lastly, internalization entails translating explicit knowledge into tacit knowledge by acquiring tacit knowledge in practice (Nonaka & Takeuchi, 1995).
2.3. Electronic Health Records & Individual Learning
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2.4. Electronic Health Records & Organizational Learning
Although explicit research on the impact of EHRs on OL is non-existent to our knowledge, effects on the information distribution and acquiring parts of OL have been reported. Scholars point to considerable potential of EHRs for knowledge distribution (e.g. Lim & Shahid, 2017; Razzaque & Karolak, 2011; Roehrs, Costa, Righi & Farias, 2017), but they also identify limitations to knowledge transfer capabilities. Reported limitations were due to data quality issues (Hierholzer, 1992), lack of remote access and incompatible systems (Lim & Shahid, 2017). Moreover, sharing patient data can result in ethical issues (McLaughlin & Coderre, 2015; Ozair, Jamshed, Sharma & Aggarwao, 2015) and problems may arise when practitioners fail to capture tacit knowledge (McCray & Tse, 2003). Tacit knowledge is difficult to share through digital technologies (Takeuchi & Shibata, 2006) and users consequently need to be aware that information systems do not always provide complete knowledge. Lastly, a recent study shows that only a minority frequently uses EHRs to find out outcomes of patients that they handed off (Shenvi, Feupe, Yang & El-Kareh, 2018). These studies point to potential and limitations for some OL processes. However, an integrative picture of how EHRs inform OL is missing and the quantitative and conceptual approaches of existing studies lack empirical insights in underlying mechanisms.
In addition, the focus of existing articles seems dominantly technical. In contrast to these technical aspects, literature on the relationship between technology and learning suggests that social processes are important as well. Information and technology can alter social structures, such as power and position (Leonardi, 2007). In addition, Broendsted & Elkjaer (2001) emphasize the importance of interaction between technology, individuals and the community to realize OL. In order to incorporate the importance of social structures in research, Virkkunen & Kuutti (2000) recommend to focus on activity systems to theorize the impact of tools on OL. Activity theory builds on constructivist reasoning and suggests that technological changes, such as EHR systems, should be located within activity systems (see Figure 1) and theorized through congruencies and contradictions in these systems (Allen, Brown, Karanasios & Norman, 2013). Outcomes are mediated by individuals (subjects), the community, and the group’s tools, rules, and division of labor
(Cole & Engeström, 1993). The above suggests that the potential of EHRs (tool) to support OL (outcome) is dependent on congruencies and contradictions with the broader hospital setting, including its rules, community, division of labor and the healthcare professional who uses the tool. For example, theory on institutional logics suggests that prior main values of expertise are being replaced by an increasing expectancy of healthcare professionals to meet standards of efficiency and effectiveness (Goodrick &
Raey, 2011). Such influences might change how and what individuals and organizations learn.
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3. Methodology
This chapter outlines the conceptual orientation (3.1), the setting and participants (3.2), and the data collection- and analysis method (3.3) of this study.
3.1.Conceptual Orientation
The methodology of this study departs from a constructivist stance, which acknowledges people as constructors of meaning and therefore truth as socially negotiated (Tenenbaum & Driscoll, 2005). In line with this stance, qualitative methods were deployed to gain in-depth insights in these meanings (Guba & Lincoln, 1994). Informed grounded theory functioned as the methodological approach to guide the qualitative research process, which combined grounded theory methods with the use of existing theoretical frameworks. Grounded theory methods are appropriate in this study since they help to explore novel mechanisms, explain processes and generate theories (Charmaz, 2006; Lingard, Albert & Levinson, 2008). In addition, these methods fit well with constructivism (Romalho, Adams, Huggard & Hoare, 2015) and the purposeful descriptive nature of OL literature (Vera & Crossan, 2003). Through iterative study design, grounded theory supported rigor research by constant comparison of linkages between insights, selecting participants based on theoretical sampling, and using theoretical saturation as the determinant of sample size (Charmaz, 2006). In addition, grounded theory promoted reflective thinking and helped to effectively perform the important role of the researcher in case-studies (Fink, 2000; Giles, King & Lacey, 2013).
In contrast to classic grounded theory (Walls, Parahoo & Fleming, 2010), informed grounded theory describes how existing literature can be valuable in grounded theory research when it is engaged with in a critical manner (Thornberg, 2012) and when data is given priority over other input (Ramalho et al., 2015). Accordingly, this study used theoretical frameworks as sensitizing tools without hindering theory from truly emerging from empirical data. Activity theory helped to move beyond the technical- and individual level focus of existing literature by focusing on congruencies and contradictions between EHRs, OL and broader organizational elements. Since activity theory provides a descriptive framework for understanding information systems and the way they inform work activity, it fits well with the aim of this research. OL frameworks developed by Hüber (1991) and Nonaka and Takeuchi (1995) stimulated in-depth analysis of OL processes. In addition to these methodological and theoretical concepts, our change management background contributed to the focus on broader organizational elements rather than merely technical elements.
3.2.Setting and Participants
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We selected participants through iterative theoretical sampling which first focused on variety in profession, including medical specialists, nurses and residents. This enabled us to examine knowledge transfer between those with similar and with different professions. After each set of interviews, further participants were chosen based on variety in tenure and attitudes towards EHRs since these characteristics seemed relevant for perceptions of how EHRs inform learning.
3.3.Data Collection and -Analysis
Case studies were used since their multi-method approach enables theory development through stronger substantiation of constructs (Eisenhardt, 1989; Van Aken, Berends & Van der Bij, 2012). Our methods included observations, secondary data and interviews from February to May 2019. By combining observations and secondary data with interviews to examine how healthcare professionals perceive the impact of EHRs on OL, this design adheres to the complex (El-Hmoudova, 2015), socially embedded (Pentland, 1995) nature of learning. As visualized in Figure 2, we followed grounded theory in creating transcripts, initial coding and memos during data collection. This facilitated constant comparison and differentiated category development by exploring linkages between different insights in early stages and enabling more focused data collection afterwards (Holton, 2008; Willig, 2013).
3.3.1. Observations
Participant observations were conducted during working days of nurses, residents and medical specialists in all hospitals for a total of 62 hours. Participants were observed during individual practices such as (out)patient visits, and collaborative meetings such as multidisciplinary consultations and grand rounds. The latter are considered as important learning experiences and provided useful insights regarding knowledge transfer. In addition, observations served as a tool to examine the social context in line with activity theory and to find out how EHRs were used during activities. Handwritten field notes were created while observing and finalized digitally afterwards.
3.3.2. Secondary data
Secondary data in the form of documents and websites provided insights in the possibilities of the different EHR systems and the course of implementation processes. Information about EHR capabilities was gathered through information on the websites of the respective software vendors and through a study (Pragus, 2016) that compares the two main EHR systems in the Netherlands. Newspaper articles and blogs about the implementation of EHR systems informed us about the motivation behind- and complications during system implementation in the examined hospitals. The secondary data and observations provided useful contextual knowledge to enhance interview quality.
3.3.3. Interviews
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first the participant was asked: ‘How do EHRs help you to learn and expand your knowledge?’. Only after this open question and corresponding probing questions, in-depth questions based on literature followed, such as: ‘To what extent do you feel that patient information saved in the EHR is useful for future similar cases?’. We iteratively adapted the content of the interview guide to emerging insights. All interviews were conducted in Dutch in order to deliver more authentic data from the native Dutch speakers. The number of interviews was determined by achievement of theoretical saturation, where no new information was found. Table 1 shows the final composition of participants. To guarantee anonymity, fictitious names are used in the remaining parts of this paper without linking to Table 1.
3.3.4. Data analysis method
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4. Findings & Analysis
‘I have said from the beginning: it is a life-event. There are important phases in your life; move to a new city, a wedding, a family member who dies… Well this EHR is definitely a life-event. It tremendously impacts your work procedures, the content of your work, how you get things done.’ [John, medical specialist]
This quote illustrates the widespread impact of EHRs on work practices of healthcare professionals. The findings show that this impact can have both positive and negative consequences for OL. This chapter presents and analyses these findings structured by Huber’s (1991) four OL constructs (section 4.2 – 4.5). Section 4.1 describes how this chapter iterates between data and interpretation.
4.1. Data Aggregation
As visualized in Figure 4 and in line with the deductive part of the coding process (Appendix 2), results are presented and analyzed with the use of three theoretical frameworks: activity theory, Huber’s (1990) OL constructs and Nonaka & Takeuchi’s (1995) knowledge distribution processes. Each of the upcoming sections describes identified mechanisms and OL outcomes that relate to one of Huber’s (1991) four constructs. OL
outcomes are discussed in the context of technical- and organizational activity theory elements to theorize about the mechanisms that enforce or inhibit OL outcomes. Enforcing elements are elements in the activity system that spur positive OL outcomes from the EHR, while inhibiting elements are elements that add to more negative OL outcomes.
Differences between hospitals, EHR systems, and data collection methods are discussed when relevant.
4.2.Knowledge Acquisition
As visualized in Figure 5, EHRs negatively influenced knowledge acquisition processes by limiting contact with patients. This section discusses how patient contact decreased and how this influenced knowledge- and skill acquisition. Although knowledge acquisition and -distribution processes are interlinked, this section focuses on processes through which individuals acquire knowledge without interaction with colleagues, while section 4.3 focuses on interactional processes.
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always been part of the job, but participants saw the administrative burden grow as a result of the EHR. To the question how EHRs change the profession of physicians, Thomas (resident) answers:
‘A study in Amsterdam revealed that residents use 80% of their time on meetings and administration. I think that must be approximately right. A regular working day lasts 10 hours and when I spend 2 hours with patients then that is a lot. You just need to click and organize so much in the EHR.’
In combination with already existing time pressures that characterize the community, this administrative burden impedes patient contact.
‘You need to document so much in the system, that sometimes on busy days you cannot get patients out of their beds just because of the administrative fuss that comes with it. Then I go home with a really dissatisfied feeling, because that is just not the way it should be. Then you just failed.’ [Helen, nurse]
In terms of quality of contact, the monitors that are needed to display the tool form a physical barrier between healthcare professional and patient. The fragment below illustrates the difficulties that healthcare professionals experience in combining technology and patient care.
John [medical specialist] asks the patient about who looked at his aneurysm. The patient says that he does not know. He is clearly frustrated and mentions that it is all unclear to him and that he does not even know why he is here. He went through a rough period and thought that he would not survive, but still nothing is clear about the reasons why. John is focused on the monitor in front of him and does not respond, he needs to find information that is not there.
Two OL outcomes relate to this decreased patient contact. First, decreased physical interaction between practitioner and patient reduced opportunities to gain and practice patient oriented competences. As the fragment below shows, residents feel that combining EHR use and patient care leads to conflicting demands in education. They simultaneously need to use the system and learn how to make contact.
After the grand round, Susan and Sharon [residents] compare the use of computers on wheels (C.O.W.) to the use of paper. ‘Paper was much better. Now you need to deal with conflicting demands: for example, you are educated to sit on eye level with the patient to make contact, but that is not even possible with a C.O.W. in front of you.’
Second, ‘People are completely drawn into the system’ [John, medical specialist], which distracts them from learning by doing. The quote below shows the frustration of a supervisor about how residents prioritize the computer over learning experiences.
‘It is terrible! You only see the patient once, you can sit behind your computer all your life, all day. The purpose of such grand rounds is bed-side teaching, but they focus way too much on that computer and too little on learning how to communicate with patients.’ [Mark, medical specialist]
All residents do say that they learn the most from working with patients, but as a result of the administrative burden they experience less opportunities to do so.
‘Unfortunately, we have less contact with patients as a result of all the administration. (…) I learn the most from practice, but we have less opportunities to go to the patient.’ [Susan, resident] ‘When we go to approximately 20 patients, you simply cannot remember everything. You need to write it down, otherwise you forget. But he [supervisor] focuses more on doing. And that is also really fun. He wants to teach stuff and that is not possible of course when you are standing behind the computer. But at the same time I think that the patient comes first and that it is important that everything is reported correctly in the EHR. But it is difficult, yes.’ [Sharon, resident]
The strength of these mechanisms differed across participants as a result of differences in use and personal characteristics of the subject. John [medical specialist] feels that EHRs do not automatically fit with the needs of healthcare practice and that practices need to be adjusted to serve the EHR rather than the other way around: ‘I feel like the EHR system is number one and we need to figure out how to integrate
patient care into it.’ Physical placement of the monitors and the timing of entering information in EHRs
strongly affect how the technology influences patient contact. The quotes below show the considerations of medical specialists in how to use the EHR system.
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mean that the patient leaves the room earlier, but I rather have 12 minutes of really good contact with my patient instead of talking via my computer for 15 minutes.’ [Robert, Medical specialist]
Personal characteristics also played a role: the effect was stronger for less experienced employees, since observations showed that they generally spend more time on reporting in EHRs than those with more experience. Those with more experience generally use only the parts of the system that they find useful or which are obligatory, while those with less experience are more likely to use all the parts that they are supposed to. In addition, the effect is stronger during the first phases of use after EHR implementation or after employment. In the beginning, learning about the EHR system competes with medical learning. This especially has a large impact on residents who need to rotate across different hospitals. Acquiring medical knowledge and skills requires a longer start-up phase:
‘In the beginning I need to put much effort in getting to know the EHR, so then I medically learn less of course. (…) For example, when I started here on the first aid. Normally I can do quite some patients on my own, but now I could handle only a few. And seeing patients: that is how I learn. So, I need to learn about the system first before I can really start to learn.’ [Susan, resident]
4.3.Knowledge Distribution
As Figure 6 summarizes, EHRs positively influenced knowledge distribution by reducing the extent of physical interaction needed for knowledge transfer. Nonaka & Takeuchi’s (1995) socialization, combination and externalization can be recognized.
First, EHRs spur socialization through reducing barriers to knowledge distribution, enabling interdisciplinary knowledge sharing and enabling supervision. Barriers to knowledge distribution are reduced as a result of dealing with hierarchical impediments and dispersion of knowledge. Integrated EHR systems make it easier to distribute and retrieve information quickly.
‘Back in the days we had paper records, which nobody could read or which were lost. Then you needed 2 days to get all the information you needed, while now all the information of a patient is accessible in the system.’ [Paul, resident]
In addition, the quote below shows how information available in the tool can reduce impediments of hierarchy in the community on asking questions and corresponding learning.
‘There are definitely differences in status. (…) It does make it more difficult to ask questions sometimes.’ [Sharon, resident] ‘You now learn more because the EHR gives you a more complete picture. (…) Back in the days you were dependent on whether you would brainstorm together with the medical specialist. Some do and some don’t because they do not feel confident enough. Now, everything is available in the system and its on you to decide whether you find it interesting enough to read it. When I look at the lab reports and I see something unusual, than I will try to figure out what the reason is and it also provides more concrete input to ask questions.’ [Sarah, nurse]
The availability of information without the need for physical interaction also helps in learning from colleagues and enables supervision. Given the time constraints in the healthcare community, the opportunity to teach and brainstorm without need for physical interaction is valuable.
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grow in their role. That comes with a lot of monitoring and sometimes I think: let’s take a look in the EHR what she reported about a certain patient.’ [Lisa, nurse]
Second, EHRs enable learning through combination of knowledge with interdisciplinary knowledge sharing and bundling of expertise. Sarah [nurse] pointed to the more integrated nature of the EHR system as compared to previous systems. For example, medical records and nursing records were separate. Now information is centered around patients and it is possible to see information of all disciplines. As everything is in the same system now, system knowledge also forms a less explicit burden to finding information from different disciplines.
‘Before the EHR was implemented you could also see information from for example cardiology, but you needed to request it or open up a different system. But for cardiology I basically only looked at the final letter, because I did not know how to use that other system.’ [Paul, resident]
EHRs also allow for bundling expertise by offering the possibility to save and reuse knowledge of different specialists. Using the best standard text for each specialty can help to bring care to a higher level and thus learn on an organizational level.
‘What has become way easier is to make standard texts, for example a text for anamnesis. [Person X] knows all about IgA-nephropathy, [Person Y] has polycystic kidney diseases as a specialty and I really enjoy electrolyte and acid base disorders. If you make a complete and neat anamnesis for disease A, B, C, then I can help them with my expertise and they can help me with theirs. That helps a lot.’ [Robert, medical specialist]
Third, in addition to sharing explicit knowledge, EHRs also enable externalization of tacit knowledge. The following fragment shows how the system provides the opportunity to share gut feelings and how discussions can help to learn from it.
Sarah [nurse] measures the patient’s heartrate and blood pressure. She reports her findings in the EHR and fills in an activity plan. In this activity plan the EHR asks her to answer some questions regarding objective measures (e.g. does the patient have a stable heart rate?), but also an early warning EWSC function. This function is an indication for the nurses’ ‘gut feeling’. When I ask her about it during our interview later that day, she explains: ‘If you see a patient during the day and you see the values changing, it can give you a worried feeling. This function is about being alert and not missing certain symptoms. The function gives you input to discuss your feelings with the medical specialist and when the medical specialist sees things differently, then you can learn from that. This functionality was added in the system because not all patients with certain values are actually stable. It makes it less ‘hard’ so to say‘.
Technical elements of EHRs put boundaries on the benefits of improved knowledge distribution. These elements include privacy issues, limited information quality, and system limitations of the tool. As the quotes below illustrate, privacy issues limit healthcare professionals to access information only from patients with whom they have a treatment relationship. In one of the systems examined in this study, the system required a reason for excessing other information and names were linked to both actions and information retrieval. In other hospitals some participants said that sometimes they do access other information since it can be beneficial for their own medical learning process.
‘I can access a lot of information that is none of my business. (…) I can access all information from the ophthalmologist and the gynecologist, I can see everything. And you have to trust me that I won’t do that. But I can. And it does provide enormous benefits.’ [Jennifer, medical specialist] ‘In theory, we are not supposed to search for random patient information, you need to have a treatment relationship with the patient. Sometimes I do it out of medical interest, but in theory it is not allowed.’ [Sharon, resident]
Limited information quality results from time lags between information gathering and entering it in the EHR, limited details in descriptions, mistakes, and information that is not reported the EHR. In addition, EHR content rarely moves beyond patient knowledge to medical knowledge. Own initiative is needed to interpret the information that is shared and to derive deeper insights.
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automatically linked and you need to understand why certain blood examinations were done for example.’ [Helen, nurse]
Moreover, most participants agree that the system is fairly complicated, with limited search abilities. One of the participants compares it with the way to Rome, illustrating the overload of different functionalities and tracks leading to the application that you need. Especially elderly employees experienced difficulties in finding needed information.
During our first meeting, John [medical specialist] notes that heaps of information are available in EHRs, but nobody knows how to find it and search possibilities are limited: ‘People often say that there are multiple routes leading to Rome. Well, in this EHR system you need to know which road to take from each single village to another to know how to actually reach Rome’.
4.4.Knowledge Interpretation
Figure 7 summarizes how increasing standardization and intelligent functionalities of EHRs influence knowledge interpretation.
The examined EHR tools to differing extends include standardized checklists to guide work practices and intelligent functionalities in the form of pop-ups that warn healthcare professionals or make suggestions. Interesting is the discrepancy between secondary- and primary data. While a study conducted by Pragus (2016) concluded that the system with a higher degree of standardization and intelligent functionalities, was the preferred EHR system, the empirical data of this study showed the opposite. Reason for this discrepancy could be the difference in participants: where this study focuses on healthcare practitioners, the other research only included 3% practitioners in their sample. As the following fragment illustrates, standardization mainly serves higher levels of the organization rather than practitioners.
A conversation emerges in which differences between system A and system B are discussed. According to one of the participants of this discussion ‘System A forces you into bounded doctrines to a higher extend. That is very useful for all kinds of statistics and research, but not for patient care!’.
EHR data and statistics serve organizational learning, since they help to turn patient information into medical knowledge. Data forms input to improve practices and enhance skills over time.
‘If we make a quarterly report we look at for example postoperative pain score. Through EHR data we can see whether that score is actually measured and whether the score matches the guideline. If not, we can take improvement measures and monitor whether these measures have the desired impact over time. (…) In that sense, it can learn us as an organization a lot about quality of care.’ [Jennifer, medical specialist] However, the impact of standardization and intelligent functionalities on individual knowledge interpretation and clinical reasoning seems less positive. Participants suggest that the lists and pop-ups send the wrong signal: numbers and standard steps are not always most important.
‘Individual patients cannot be standardized that easily. That is my objection against all the standard checklists, sometimes people seem to forget to think. (…) It sends a wrong signal: as if the checklists are what is important, but it is not the complete picture of course. [Paul, resident]
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‘The system is so focused on just getting the scores right… Well reality is just not that simple. So EHRs and learning… I think it is mostly the opposite. Mostly the opposite.’ [Helen, nurse] ‘That will really diminish, clinical intuition. This morning I tried to teach them: go look at the patient first, think for yourself. That man with shortness of breath that we saw this morning? When we saw him in the afternoon the problem was solved: he needed to go to the toilet. That is typical geriatric care, masked symptoms. You do not learn that from a checklist, you need to go to the patient.’ [Mark, medical specialist]
Checklists are sometimes even explicitly used as replacement for education, as illustrated in this quote. ‘It is a bit like a car mechanic who knows how to recognize engine problems from information in the computer system, but who does not know how to replace a tire. (…) The EHR system does not improve it, it only makes it worse. When the geriatric department wants more attention for falling, then we do not get a training day anymore: we get a new checklist. If the patient is older than 65, I check a box and need to answer another 20 questions. But why do I need to do all of that, I see the patient right in front of me!’ [Kim, nurse]
Participants express their worries about the effects of the guidance of the system on self-thinking abilities. As a result of the system taking over some of the thought processes, human thinking might be inhibited as illustrated by the following metaphor of learning to ride a bike.
‘I just talked to some colleagues about this. I think you create stupid people with it. They will rely too much on the system and think: if there is no pop-up I can proceed. One of the topics of our conversation was the self-thinking abilities of residents about infectious diseases. EHR does not do that. Actually, it warns you for everything. How can you learn to ride your bike if you are not allowed to ride through the city and when somebody continuously yells: watch out, something is coming from the right! And now left! How could you ever see risks yourself?’ [Mark, medical specialist]
Two organizational elements are particularly relevant in the context of this standardization: rules and community. The community is characterized by desires for autonomy, in contrast to the increasing rules and standardization in healthcare. The fragment below illustrates how EHRs limit the freedom in proceeding work practices according to own preferences and specific cases.
John [medical specialist] needs to answer a question in the EHR system. The insurance company requires four measurements while there are only two measurements available. He answers: then we will have to wait. Purple crocodile [Dutch symbol for red tape and bureaucracy]. He says: ’There are way too many rules. EHRs reinforce that trend by allowing more registrations and measurements. (…) Actually, you need to work differently, but because the system says otherwise you need to just go along with it. It influences your way of working. It would be better to think whether something is relevant for a specific situation.’ Another respondent emphasizes that standardization does not necessarily only leads to negative consequences and that medical specialists might just overestimate their own powers.
‘Well you can think about standardization what you want, but I think the big problem is… I think it is perfectly clear that if there is one profession that always thinks to know it better and that does not let themselves be standardized: than it is physicians. So in fact we are a very stubborn bunch of people that find themselves extremely intelligent. And those people find it difficult to adhere to standardization, well so be it. I think it can also be very useful.’ [Robert, medical specialist]
In addition, how the subject uses the EHR plays a role in the extent to which healthcare professionals let the system influence their work practices. While some EHR functionalities force the user to follow particular guidelines, other functionalities can be closed or skipped. For the latter, most experienced professionals often decide not to use them, while for less experienced professionals they form a stronger influence on their work.
‘I just do my thing like I always did. (…) I close pop-ups. Well, the important ones I know. For less experienced employees it can be important, then you really need to pay attention. And besides, there might also be a few that I do not know as well. But that is the problem with how we structure it currently: for each miniscule thing we get a warning. Then you create alert fatigue.’ [Robert, medical specialist]
4.5. Organizational Memory
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standardization and digitalized medical memory. In turn, healthcare professionals may become more dependent on technology and may have more difficulties with being creative in unusual cases.
‘But if you always get protocol A suggested as the way to go, you can never think of protocol B being more beneficial for a specific patient. And if you always get protocol A suggested as the way to go, do you eventually even know what is in the protocols? Ask any young person about the phone numbers of their friends. Nobody knows! They can call they friends by pushing a button, but they are totally lost when they lose their phone.’ [Jennifer, medical specialist]
As this example illustrates, digitalized memory can become problematic when the EHR system is not available 100% of the time.
‘Last weekend, we needed to deal with a breakdown of the EHR system. In the past we had a little book in which we could find the antibiotics policy. (…) This weekend, the app in which we can now find the policy malfunctioned. Well, the resident did not know which antibiotics to prescribe, because of the system breakdown. That breakdown lasted an entire day. Luckily I knew.’ [Jennifer, medical specialist]
Also, limited EHR search options in current EHR systems place boundaries on the effectiveness of this technological memory in practice. Information can only be used for future cases when patient name and date of birth are remembered or saved. It is not possible to search for certain diseases.
‘If I want to search for information about a patient from the past that had similar issues as my current patient, I need to know the name and date of birth. And even if you remember, you might have a Mr. Jansen who is between 30 and 40 years old. That’s impossible.’ [Susan, resident]
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5. Discussion & Conclusion
This chapter discusses the findings and impact of this study, moving from specific findings (5.1) to broader theoretical interpretation (5.2), strengths and limitations (5.3), implications (5.4), and conclusions (5.5).
5.1. Principal Findings & Meaning
This study originated from the need to gain broader insights in how EHRs inform OL in today’s turbulent healthcare environment. Given the informative nature of EHRs and optimistic notions from prior literature, we expected to find a positive impact of EHRs on learning. Surprisingly, this study dominantly pointed towards negative mechanisms. In line with prior literature (Roehrs et al., 2017; Razzaque & Karolak, 2011; Lim & Shahid, 2017), our findings do suggest that EHRs positively influence knowledge distribution processes through improved information access. Also, EHR data facilitated knowledge interpretation on the organizational level by turning information into medical knowledge. However, individual knowledge acquisition and knowledge interpretation outcomes were less promising. Practitioners expressed their concerns about how EHRs decreased contact with patients. This hampered knowledge- and skill acquisition through reduced opportunities to develop patient-centered competences and to learn by doing. Furthermore, standardization and intelligent functionalities in EHRs seemed to reduce clinical reasoning competences and trigger a shift from human- towards system memory. Thus, do EHRs navigate healthcare practitioners towards excellent care? EHR systems clearly come with enormous benefits, but in the context of learning this study shed light on some reservations. The latter cautionary results deviate from prior empirical literature (Atwater et al., 2016; Habboush et al., 2018) which positively emphasized EHR-related opportunities for individual resident education. Our alarming results relate more closely to the conceptual suggestions of Peled et al. (2009), who worried about negative consequences of EHRs for learning. Differences from prior empirical studies (Atwater et al., 2016; Habboush et al., 2018) can be explained by the broader focus of this research. While previous literature focused on the EHR as a tool for education of individual residents, this study examined broader types of learning and added collective levels of learning. In addition, the use of activity theory enabled theorizing the impact of EHRs on OL in the context of important organizational elements rather than only technical elements. All of these characteristics contributed to filling the gap through providing a broader understanding of EHR’s impact on learning, which uncovered several conflicts and corresponding negative learning outcomes.
5.2.Theoretical Interpretation
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being inherent to EHRs in general. In addition, the subject’s use behavior largely mediated how EHRs informed OL, with physical placement and timing of recording information as important indicators.
Another explanation for limited realization of learning potential is the preliminary nature of the shift from human- towards technological intelligence. Some “organizations know less than their members do” (Argyris & Schön, 1996, p.6), while other “organizations seem to know far more than its individual members” (Argyris & Schön, 1996, p.7). The EHR stimulates a shift towards the latter, in which knowledge and skills are standardized and stored in EHR systems rather than in human minds. This transition can be compared to the more advanced emergence of TomTom navigation. The struggle of figuring out geographical charts is in the past since TomTom direction statements simply guide us through the junctions and exits leading towards the final destination. However, when getting out of the car most people do not remember any of the street names through which they drove only a few minutes ago. Our results suggest that this ‘TomTom effect’ begins to transpire in EHR use, but today’s EHR systems are not (yet) advanced enough to provide complete navigation in care. As a result, technology overreliance is dangerous. Over time, developments in EHR intelligence might mitigate our alarming findings.
5.3.Strengths & Limitations
The rigorous combination of secondary data, observations, and interviews for data collection deepened insights especially since learning is a largely unconscious process (Maslow, 1970). Using observations as input for interview questions helped participants to reflect on EHR’s role in that process. Second, the iterative data collection and analysis allowed for early recognition of patterns and stronger substantiation of categories as they could be tested with new data. Third, activity theory proved to be a useful framework to generate deeper insights in the contextual circumstances of the examined mechanisms. In contrast to standard sociotechnical perspectives, activity theory makes aspects of social structures and culture explicit (Allen et al., 2013) by distinguishing rules and norms, community and division of labor. These elements proved particularly useful for theorizing about the influence of EHRs on OL.
Some limitations need to be considered. Our choice to examine OL processes only from one department, might constrain generalizability to different hospital settings. The examined internal medicine context is characterized by the need for clinical reasoning skills to solve medical puzzles. In contrast, surgical professions might depend less on clinical reasoning and more on technical skills. Standardized procedures might be more beneficial in such contexts. Second, although we deliberately adjusted research methods to the largely unconscious nature of learning, this factor remains a potential cause for missing mechanisms. This means that the identified mechanisms in this study might not be fully exhaustive.
5.4. Practical Implications
The results imply that we should be careful with how we structure and use EHRs in order to reduce constraints in knowledge acquisition, -interpretation and memory. Lessons that practitioners can take from this study are three-fold. First, practitioners should be aware of trade-offs and should carefully balance the need for standardization and human learning. This study showed how adding standardized checklists can be a tempting solution for shortcomings in care. However, standardization simultaneously seemed to reduce clinical reasoning and human memory. Until standardized EHR tools provide complete navigation in care, we discourage replacement of human education with additional checklists.
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attention to how healthcare professionals can combine EHRs with their daily practices. Additional training could provide insights in how daily practices can be structured differently to record information after patient contact and to position computer screens in ways that do not block contact between patient and practitioner. As an alternative, medical scribes could be used for alleviating the burden of EHR on patient-clinician interactions (Schultz & Holmstrom, 2015).
Third, reducing barriers to access information in EHRs can improve learning outcomes for individual practitioners and strengthen the hospital’s ability to translate information into medical knowledge. Further advancements of information quality and system functionalities such as search options are key to advance accessibility. In addition, although privacy is clearly of utmost importance, agreements about system authorizations should also reflect the learning opportunities related to organization-wide information access.
5.5. Future Research
This study adds to our knowledge by providing a broader understanding of how EHR influences learning and adds a more critical perspective to previous literature. Given the results, we encourage scholars to explore organizational elements in addition to technical elements when theorizing about the impact of EHR. Activity theory proved to be a valuable sensitizing framework to achieve this broader focus.
Second, future research in different contexts and with different methods could further strengthen generalizability our conclusions. Given the importance of differences in EHR design, we recommend to examine whether our conclusions hold in settings with EHR systems of vendors other than Epic and Chipsoft. In addition, the EHR might inform OL differently across various disciplines and departments. As emphasized earlier, surgical professions in particular might deviate from internal medicine and would function as an interesting context to examine generalizability. Furthermore, research with different methods might also add to our current study. Given the largely unconscious nature of learning, experiments with use of think-aloud protocols might enable more thorough analysis of thoughts as a result of stimulating immediate awareness (Charters, 2003).
Third, the identified importance of how EHRs are incorporated into daily practices deserves further attention. This study revealed diverse characteristics of effective use, such as timing and physical placement. However, research with explicit focus on how EHRs are used most effectively to spur desired learning outcomes might add to this. Cognitive load theory has been applied to human-computer interaction (Hollender, Hofmann, Deneke & Schmitz, 2010) and might be a useful alternative lens to theorize how practitioners can learn when to rely on the system and when to rely on their own thoughts. Lastly, identified trade-offs between increasing technological intelligence and human intelligence suggest a need to reconceptualize OL in the digital era. Future research could explore OL frameworks that explicitly distinguish these types of intelligence.
5.6. Conclusions
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