Keywords

5.1 Introduction

The novel coronavirus SARS-CoV-2 (COVID-19) outbreak has caused a sudden surge of patients that posed a new challenge to hospitals, especially the Intensive Care Unit (ICU) (Arabi et al. 2020). The overburdened healthcare service, in response to emergencies, has highlighted the indispensability of the electronic medical recordFootnote 1 (EMR) system as a coping mechanism for physician productivity (Wang et al. 2020). Hence, this study sets out to examine how the rapidly changing healthcare environment due to the COVID-19 pandemic has challenged physicians’ technology perception and readiness as physicians have to adapt to the EMR system to improve their productivity purposefully (Cohen et al. 2020).

Since healthcare service is highly dependent on physician openness to adapt to new work processes, the transition from manual to the EMR system is more of a managerial concern than a technological consideration due to human resistance to change and potential interruption or complexity to the existing workflow operation (Zandieh et al. 2008; Nguyen et al. 2014). Specifically, in developing countries, the implementation of EMR system is still costly to most public hospitals. Thus, little is known about physician belief on how COVID-19 can accelerate the use of the EMR to aid decision-making, and the drivers of IT use behavior in the context of healthcare delivery (Lu et al. 2020).

The study integrates theoretical perspectives from the information systems (IS) and dynamic capabilities literature. We combine some key constructs from two widely used models in the literature namely the Technology Acceptance model (TAM) and Unified Theory of Acceptance (UTAUT) to analyze the physicians’ intention to use and ensuing behavior. Furthermore, the current COVID-19 pandemic presents bigger challenges to the healthcare service in terms of effectively responding to the crisis. As such, the dynamic capabilities literature presents a potential response in this turbulent environment.

First, we set out to examine the impact of the EMR use on physician productivity, mediated by physician dynamic capabilities for knowledge acquisition and deployment, during the switch from manual to electronic workflow operation at the onset of the EMR deployment in the hospital pre-pandemic (before the outbreak of COVID-19). Next, we compare the impact of the EMR use on physician productivity post-pandemic (after the outbreak of COVID-19). The secondary examination is intended to determine how the pandemic has impacted physician perspective on the use of the EMR to reveal insights into the importance of EMR that contributes to the acceleration of the digitization of healthcare services post-pandemic. Our results will critically capture lessons learned on the EMR use to leverage physician dynamic capabilities and physician productivity from a developing country perspective.

5.2 Electronic Medical Record (EMR) System in Healthcare Service

The COVID-19 pandemic has catalysed digitization in healthcare by highlighting the importance and accelerating the use of the EMR (Mahmood et al. 2020). Often, the role of physicians and patient care is inseparableFootnote 2 within the healthcare service. For instance, a patient needs to explain the symptoms of the medical condition in order for a doctor to pronounce a correct diagnosis so the service is inseparable as the patient is physically present and involved in the service.

Additionally, quality healthcare service is associated with patient mortality and readmission rates, patient satisfaction with quality care or physical health, infrastructure, and availability of preventative care (Legatum Prosperity Index 2017). Although the EMR is gradually implemented in stages by hospitals in developing countries (Dornan et al. 2019; Fraser et al. 2005), the costs associated with healthcare technology investment has not been uniformly justified, often leading to slow adoption of the EMR (Terry et al. 2012).

As the EMR moves healthcare information and knowledge resources towards an integrated interface, effective use of the system would improve staff efficiency, accountability, and error reduction in hospitals as it systematically manages patient clinical data (Williams and Boren 2008; Jha 2010). The use of the EMR reduces human error and improves medical diagnostics as physicians do not work in silos and are able to make better patient care decisions based on available online information (Noraziani et al. 2013). In this age of rapid medical advancement, physicians have to rely on reliable systems to ensure real-time access to information and enable regular communication between teams of multidisciplinary specialists involved in the care of a patient (Evans 2016). The EMR minimizes occurrences of biasness when physicians decide on a treatment course after making a definitive diagnosis or the consequences of a wrong diagnosis since physicians will not have to second guess their decisions since all decisions will be made objectively based on the data provided by the EMR (Scott et al. 2018; Bornstein and Emler 2001).

Despite the potential benefits of using the EMR, certain challenges in using the EMR system have also been highlighted in the literature (Fraser et al. 2005; Thakkar and Davis 2006). Physicians have described requiring longer working time learning to use the EMR effectively as most systems have multiple screens and navigation options. With an already heavy workload, physicians would prefer spending their time by the patient’s bedside and getting more involved in the practical aspects of patient care (Dewa et al. 2014). Thus, many physicians tend to use the EMR system as an electronic paper record system and were unable to fully utilize the advanced functions of the EMR (Price et al. 2013).

5.3 Theory and Hypotheses Development

5.3.1 Dynamic Capabilities Theory

Rapid technological development presents an excellent potential for healthcare service organizations. However, the actual use of these technologies may be resisted by physicians for fear of changing their work processes, consequently affecting their productivity and job performance (Kankanhalli et al. 2016). This resistance from physicians may further impair the allocation of knowledge resources that would impact not only patient care but also socioeconomic outcomes for the healthcare organization. Existing literature often view technological infrastructure as a trajectory to effective knowledge management, thereby having a positive impact on productivity and job performance (Easterby‐Smith and Prieto 2008). As such, knowledge management literature often relies on the dynamic capabilities perspective to explain “how best to manage organizations in dynamic and discontinuous environments” (Kankanhalli et al. 2016).

In one of the earliest papers on utilizing dynamic capabilities to enhance competitiveness in the healthcare industry, Sher and Lee (2004) emphasized the “importance of dynamic capabilities in a turbulent environment.” Since the emergence of the COVID-19 pandemic, healthcare service organizations had to navigate the turbulent environment by frequently reconfigure their work processes to enhance performance. Without the right technological capabilities, the disruptive effects of the pandemic can create a severe liability to the healthcare service organization.

Dynamic capabilities are the ability of an organization to manage its vast resources in a rapidly changing environment, allowing for improvisations where necessary. The concept, first defined by Teece and Pisano (1994), was built on a resource-based view (Barney 1991) dealing with the evolutionary nature of resources and capabilities in a rapidly evolving environment. However, having resources advantage is necessary but not sufficient for gaining competitive advantages. Therefore, the enhancement of distinctive capabilities is essential to make better use of organizational resources (Wang and Ahmed 2007).

The development of dynamic capabilities can be understood as an evolving process based on the interaction between daily operations and knowledge-creating activities. It is not a one-off ad hoc problem-solving mechanism (Zahra et al. 2006). The enhancement of dynamic capabilities is an ongoing process that would redefine the knowledge base of organizations or individuals, which eventually would lead to value creation (Barreto 2010). Especially at the individual level, Rothaermel and Hess (2007) postulate that individuals interact with organizational supporting assets and processes to achieve improved performance. The heterogeneity of cognitive capability such as medical diagnosis evidenced support of “dynamic managerial capabilities for sensing, seizing, and reconfiguring, and explained their potential impact on strategic change of organizations” (Helfat and Peteraf 2015).

5.3.2 A Unified Theory of Acceptance and Use of Technology (UTAUT2)

The UTAUT2 model is an extension to the original technology acceptance model (TAM) and UTAUT model but refined to include consumer use context (Venkatesh et al. 2012). ‘Perceived usefulness’ and ‘perceived ease of use’ from TAM remain as main predictors in the UTAUT and UTAUT2 for behavioral intention to use (Venkatesh et al. 2011). However, the subsequent UTAUT models use different terminologies ‘performance expectancy’ and ‘effort expectancy’ while maintaining the same conceptual underpinning of the TAM constructs. Habit is one of the new key constructs incorporated into the UTAUT2 model. In line with Limayem et al. (2007), the authors define habit “as the extent to which people tend to perform behaviors automatically because of learning” (Venkatesh et al. 2012). As such, the authors posit that habit can influence technology perception and the intention to use a system.

This study included perceived usefulness (PU) and perceived ease of use (PEU) from the common TAM and UTAUT frameworks but excluded social influence (SI) and facilitating conditions (FC) postulated in the UTAUT frameworks. It is because our study took a person-centric approach in examining individual physicians’ perceptions and focused on two different stages of the EMR use from adoption to post-adoption. Furthermore, the sudden surge of patients, especially in the ICUs (Arabi et al. 2020), might affect both new and experienced physicians in their EMR use behaviors. Hence, we investigate the change in physicians’ perceptions of using the EMR system from pre-pandemic to post-pandemic (after the outbreak of COVID-19).

5.3.3 Conceptual Framework

Based on the information systems and dynamic capabilities literature discussed above, the conceptual model is illustrated in Fig. 5.1. Accordingly, the study examines whether technology perception (perceived usefulness and perceived ease of use) as antecedents of the intention to use EMR, would also impact dynamic capabilities and physician productivity. Following Venkatesh et al. (2012), we added habit into the model as a direct antecedent of intention to use and a control variable to moderate the relationship between intention to use and physician productivity.

Fig. 5.1
figure 1

Conceptual model

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5.3.3.1 Technology Perception on Intention to Use

The information systems research identifies two technology perception successfully used to explain actual technology acceptance and system use (Venkatesh et al. 2003). Perceived usefulness (PU) and perceived ease of use (PEU) are two core beliefs in Davis’ (1989) Technology Acceptance Model (TAM), one of the most widely used models in studying individual-level system adoption and use. Moreover, the conceptual underpinning of PU and PEU also incorporates into the UTAUT and UTAUT2 models (Venkatesh et al. 2003, 2012). In this study, PU is defined as the degree to which an individual physician believes that using an EMR would be useful to enhance job performance (Davis 1989). PEU is defined as the degree to which an individual physician believes that using an EMR would be easy and free from effort (Davis 1989). PU has always been the primary determinant of system adoption and use.

With an already busy workload, the implementation of the EMR may exacerbate the burden on physicians who show significant concerns over their responsibilities for medical decisions, clinical documentation, patient care, administration issues, and research especially in teaching hospitals (Bhargava and Mishra 2014; Sykes et al. 2011). The field survey by Ayanso et al. (2015) demonstrates that PU directly impacts the physicians’ continuance intention. This study postulates that physicians who find the EMR useful for their operational routine and performance benefits will use the system regularly (Dobrzykowski and Tarafdar 2017). Thus, hypotheses 1 are as follows:

H1:

Perceived usefulness (PU) has a direct positive effect on intention to use the EMR (INT).

The design of most EMRs could be cumbersome with multiple viewing screens and rigid navigation options that are not customized nor user-friendly for physicians. The initial learning time may put off physicians in transiting from their habitual use of the manual system to the new EMR. Furthermore, some physicians may perceive that learning to use and master a new EMR is a bad trade-off as they would rather spend their time being more involved in-patient care. Although Sykes et al. (2011) find that only PU is a key factor influencing physicians’ use of the EMR, the study also points out the potential effect of PEU on the intention to use. Gagnon et al. (2014) evidence that PEU is positively associated with the physicians’ intention to use the EMR. This study postulates that physicians who find it easy and comfortable to use the EMR would likely use it at least as regularly as they do now and for the foreseeable future (Price et al. 2013; Jha 2010). Thus, hypotheses 2 are as follows:

H2:

Perceived ease of use (PEU) has a direct positive effect on intention to use the EMR (INT).

Davis (1989) and Venkatesh et al. (2003) discover that PEU can directly impact PU for technology adoption and use, but not otherwise. The positive correlation between PEU and PU is also observed for the intention to use EMR (Sykes et al. 2011). This study postulates that physicians who find it easy and comfortable to use the EMR would be likely to overcome the perceived difficulty in using it and willing to spend time learning to master any useful aspects of the system (Terry et al. 2012; Tierney et al. 2013). Hence, hypothesis 3 is as follows:

H3:

Perceived ease of use (PEU) has a direct positive effect on perceived usefulness (PU) of using the EMR.

The intention to use EMR is measured by behavioral intention, which accounts for the internal behavioral determinants of physicians, as the degree to which conscious plan is formulated in the mind to use or not to use EMR in the future (Warshaw and Davis 1985). Given the integral role of EMR in the physicians’ decision-making process, the intention to use EMR beyond adoption could directly affect physicians’ dynamic capabilities and productivity. This study postulates that a positive intention to use the EMR would have a direct impact on physicians’ abilities to acquire and learn new knowledge from other physicians’ inputs to the system, integrate and coordinate available information resources to improve decision quality on critical patients. Furthermore, a positive intention to use the EMR would also directly affect physicians’ productivity as they synthesize information from multiple sources on the EMR and make patient treatment decisions efficiently, which can be helpful to other physicians (Nguyen et al. 2014; Furukawa 2011). Hence, hypothesis 4a and 4b are as follows:

H4a:

The intention to use EMR (INT) has a direct positive effect on dynamic capabilities (DC).

H4b:

The intention to use EMR (INT) has a direct positive effect on physician productivity (PP).

5.3.3.2 Dynamic Capabilities on Physician Productivity

In the healthcare industry, physicians should not only be able to perform their duties (operational capabilities) but comprehend changes in their external environment, seize the opportunity to utilize technology and knowledge resources or competencies to meet new challenges (Teece 2007). The quality of the job performed by physicians would directly ensure the quality of healthcare services provided, which may further impact patient care and socioeconomic outcomes at the macro-level.

Furthermore, as learning is central to developing dynamic capabilities, physicians often “learn by doing” and are called to provide accurate diagnosis involving life-and-death decisions (Cohen and Levinthal 1990). It is as they learn by accessing explicit patient data and assessing these data based on tacit medical knowledge that they can articulate sound diagnosis on patients. Trial and error, improvisation, and imitation (Zahra et al. 2006) may also be useful in the process of reconfiguring knowledge resources and competencies to address the medical conditions at hand better. Physicians are learning and consciously developing their dynamic capabilities for better productivity and job performance when they are engaging in such knowledge-creating activities (Easterby‐Smith and Prieto 2008).

The EMR helps to facilitate patient information and consolidate multidisciplinary knowledge resources in the hospital. This study postulates that using the EMR would be likely to enhance physicians’ dynamic capabilities for knowledge acquisition and deployment in practical aspects of patient care, and thus having a positive impact on physician productivity (Agwunobi and Osborne 2016). Hence, hypothesis 5 is as follows.

H5:

Dynamic capabilities (DC) has a direct positive effect on physician productivity (PP).

5.3.3.3 Habit, Intention to Use and Physician Productivity

The UTAUT2 model incorporated habit as a perceptual construct that reflects prior experiences (Venkatesh et al. 2012). Physicians form different levels of habit depending on the extent of their interaction and familiarity when learning to use an EMR and receiving feedback from prior experiences (Limayem et al. 2007). Accordingly, habit is postulated as one of the “key drivers of behavior” (Venkatesh et al. 2012) that would directly impact intention to use as well as negatively moderating the effect of intention on actual use behaviors. Such operationalization of habit is consistent with the UTAUT2 model, which believes that habit influences behavioral intention and increasing habit would diminish the effect of intention on actual use (Limayem et al. 2007; Venkatesh et al. 2012). Thus, it is necessary to investigate not only the positive effect of habit on the intention to use EMR but also whether habit would weaken the intention to use EMR for physician’s productivity. Hence, hypotheses 6a and 6b are as follows:

H6a:

Habit (HBT) has a direct positive effect on the intention to use EMR (INT).

H6b:

Habit (HBT) negatively moderates the relationship between intention to use EMR (INT) and physician productivity (PP).

5.4 Methodology

5.4.1 Data and Sampling Design

This study is a subset of more extensive research conducted in the Intensive Care Units (ICUs) of four leading tertiary referral hospitals in Malaysia. These hospitals are directly involved in the screening and intensive care management of COVID-19 positive patients, Patients Under Investigation (PUI), and those with Severe Acute Respiratory Infection (SARI). The Malaysian Society of Intensive Care Medicine strives to provide optimal medical expertise and excellent patient care in every hospital.

The data were collected in two waves. The pre-pandemic data (wave 1) was collected in 2018, representing the ‘normal’ use of EMR while the post-pandemic data was collected from April-May 2020 (wave 2), representing the ‘abnormal’ use of EMR during the spike of COVID-19 cases in Malaysia. The study focuses on individual physicians who are at the frontline of interactions with the EMR reviewing patients’ information, making decisions, executing treatments, and entering patients’ updates into the system (Bhargava and Mishra 2014).

Clustered random sampling method was employed to collect data pre- and post-pandemic, reaching out to all physicians (in the categories of specialists, medical officers, and house officers) who are directly involved with patient care in the ICUs of the identified hospitals. The sampling frame contains a list of all physicians working in the ICUs during the period of our data collection. The physicians were given the option to complete either a paper-based or an online-based questionnaire during the pre-pandemic survey. During the post-pandemic survey, we reached out to physicians who had previously completed the first wave of the survey as well as other physicians who were newly added to the sampling frame. All respondents were given the only option of an online-based questionnaire during the post-pandemic survey. Our survey questions were part of a larger survey administrated in the hospitals.

A total of 67 valid responses and 55 valid responses were obtained from pre-pandemic and post-pandemic waves, respectively, representing a majority of the ICU physicians in the country. Results from the cross-tabulations and independent samples t-test (see Table 5.1) indicated no significant differences of the sample characteristics were found between pre-pandemic respondents (wave 1, n = 67) and post-pandemic respondents (wave 2, n = 55). The final sample consists of 122 physicians.

Table 5.1 Sample characteristics: pre-pandemic and post-pandemic comparison
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5.4.2 Content Validity

To ensure the content validity of the multi-item scales used in the study, readily established instruments were adapted from prior research. Instruments measuring perceived ease of use (PEU) and perceived usefulness (PU) of using the EMR were obtained from Sykes et al. (2011). Sample items include “Learning to operate EMR would be easy for me” and “I believe EMR would be useful in my job”. Habit (HBT) and intention to use EMR (INT) were measured using Venkatesh et al.’s (2012) UTAUT2 instruments such as “The use of EMR has become a habit for me” and “I intend to use EMR for the foreseeable future”. Instruments measuring dynamic capabilities enhancement (DC) were obtained from Sher and Lee (2004). Sample items include “EMR enhances learning effectiveness of new knowledge” and “EMR enhances decision quality”. Instruments measuring physician productivity (PP) were obtained from Bhargava and Mishra (2014). The sample item includes “EMR allows me to make patent treatment decisions efficiently”. All items were measured on a 7-point Likert scale except for PP, which was measured on a 5-point Likert scale (refer to Appendix for full list of survey instruments).

5.4.3 Results

The empirical results were obtained from a multivariate analysis based on the partial least square structural equation modeling technique (PLS-SEM). The SmartPLS 3 software was used to confirm the measurement model and estimate the structural model (Ringle et al. 2015). The PLS-SEM is useful in handling models with small sample size and makes no distributional assumptions (Hair et al. 2017). The measurement model and structural model were also re-analyzed using a bootstrapping procedure of 5000 resamples to prevent overestimating the test statistics and increase estimation confidence based on the distribution-free sampling method (Hair et al. 2017).

5.4.3.1 Measurement Model

The measurement model was evaluated to ensure for construct reliability, convergent validity, and discriminant validity (see Table 5.2). The construct reliability and convergent validity were ascertained with all item loadings above 0.708, composite reliability (CR) higher than the threshold value of 0.70, and average variance extracted (AVE) higher than the threshold value of 0.50. Sufficient discriminant validity was also ascertained based on Henseler et al.’s (2015) heterotrait-monotrait ratio of correlations (HTMT). All inter-construct correlations were lower than the threshold value of 0.85 based on \(HTMT_{.85}\) criterion (Kline 2011). None of the two-tailed 90% bias-corrected confidence intervals of the HTMT contains the value of one (Henseler et al. 2015).

Table 5.2 Convergent validity and discriminant validity
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5.4.3.2 Structural Model

The structural model explicates the hypothesized relationships between antecedents (perceived usefulness (PU), perceived ease of use (PEU), habit (HBT)) of the intention to use EMR (INT), and its impact on dynamic capabilities (DC) and physician productivity (PP) pre- and post-pandemic. Inferences were drawn based on bootstrapped t-statistics at the specified 1%, 5%, and 10% levels of significance. Overall, the model explained 75.4% (pre) and 63.1% (post) variations of INT, 56.9% (pre) and 4.8% (post) variations of DC, 59.0% (pre) and 61.1% (post) variations of PP. Table 5.3 provides a summary of the magnitude and significance of direct path coefficients that reflect the hypothesized relationships in this study, comparing pre-pandemic and post-pandemic models. Table 5.4 further provides interpretation of our results.

Table 5.3 Results of hypothesized relationships (pre-and post-pandemic)
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Table 5.4 Interpretation of results (pre- and post-pandemic)
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The measurement invariance of the composite models (MICOM) procedure was performed to ascertain the presence of observed heterogeneity in the study. The MICOM established that our empirical model achieves configural invariance and composite invariance based on 1000 permutations with two-tailed 95% confidence intervals. The MICOM results indicated that the same construct was understood and assessed in a similar way across both groups (Chan 2011), although data collection was conducted in two different time-periods. Thus, the respondents from the pre-pandemic group and post-pandemic group measuring the same construct were validated (Bialosiewicz et al. 2013). The comparison between pre- and post-pandemic empirical models was also justified and would be meaningful.

Sufficient predictive relevance was obtained to generalize our empirical model to other studies with a similar context. Results from the blindfolding and PLSPredict procedures show that all constructs in the model have cross-validated redundancy \(Q^{2}\) values higher than the threshold of zero (Hair et al. 2017). The 10-fold cross-validation also confirmed predictive relevance of the PLS-SEM model with all \(Q^{2}\) values greater than zero, and most testing errors (RMSE, MAE, and MAPE) lower than the linear model prediction.

The empirical results supported four significant hypotheses in both the pre-pandemic and post-pandemic models, namely H1: PU → INT, H3: PEU → PU, H5: DC → PP and H6a: HBT → INT. Furthermore, two hypotheses show significant changes post-pandemic, H4a: INT → DC and H6b: INT*HBT → PP, considering physician new experience in the ICUs. We demonstrate that:

  1. i.

    Pre-pandemic: technology perceptions (perceived usefulness, perceived ease of use, and habit) significantly result in strong intention to use EMR, but such an intention did not have significant direct impact on physician productivity. Furthermore, increasing habit would diminish the effect of intention on physician productivity such that the use of EMR would become automatic with more experiences in using the EMR (significant negative moderating effect). A key relationship during the pre-pandemic use of EMR is that dynamic capabilities strongly and fully mediatesFootnote 3 the effect from intention to physician productivity (INT → DC → PP). Thus, technology perceptions leading to intention to use EMR have only a significant indirect effect on physician productivity.

  2. ii.

    Post-pandemic: technology perceptions (perceived usefulness, perceived ease of use, and habit) still significantly result in strong intention to use EMR. Such an intention now has a (very) weakly significant direct impact on physician productivity and no longer directly impact dynamic capabilities. Furthermore, habit no longer has any significant moderating effect on the relationship from intention to physician productivity. The key role of dynamic capabilities has also significantly changed from pre- to post-pandemic. Dynamic capabilities no longer mediates the effect from intention to physician productivity. Instead, dynamic capabilities now directly impact physician productivity, and this direct positive effect is much stronger post-pandemic (DC → PP).

5.5 Healthcare Service Management Insights and Lessons Learned

This study evidences two significant findings: (1) before the pandemic in the ‘normal’ condition of EMR use, technology perception has significant indirect impacts on physician productivity via the key role of dynamic capabilities. However, (2) after the pandemic in the ‘abnormal’ condition, the way physicians perceive the use of EMR no longer has any significant impact on their productivity; although their intention to use EMR may have a very weak direct impact on their productivity. A key significant change in the new norm post-pandemic is that dynamic capabilities no longer mediate but strongly and directly impact physician productivity. This direct positive effect is much stronger than before the pandemic. The COVID-19 pandemic has undoubtedly changed the way physicians perceive the use of EMR and provides insights into the knowledge of technology uses in hospitals.

Healthcare service organizations in developing countries can utilize physician dynamic capabilities as a source of competitive advantage (Agwunobi and Osborne 2016). The EMR organizes structured clinical data that helps physicians make informed decisions to provide accurate diagnosis during their performance in the ICU. Furthermore, the system is often dependent on the collective users of the system. It is because decisions in the ICU require consolidating patient data from various sources to allow physicians a holistic overview of patient condition. Huckman and Pisano (2006) examine procedures performed by 203 surgeons in Pennsylvania and find that a portion of surgeon performance is specific to the hospital. Their results suggest that physician performance is linked to the familiarity of systems in place, and their marginal productivity is not constant across firms. Thus, the implementation of the EMR justifies the cost of capital investment to healthcare service organisations and the cost of time and effort to physicians in learning the EMR system.

Our study provides practical implications in that enhancing physician dynamic capabilities can be leveraged as a source of competitive advantage in environments such as the ICU (pre-and post-pandemic). The increased use of the EMR will improve not only physician dynamic capabilities but also their productivity. Specifically, perceived usefulness and perceived ease of using the EMR would determine whether the enhancement of physician dynamic capabilities could be a source of their increased productivity. Physician productivity is strongly linked to how they utilize the EMR for building and integrating their competencies in daily operations of an intense situation in the ICU.

A distinct advantage of implementing the EMR is to move away from information silos among physicians (who are specialists in their respective fields) towards an integrated interface. Decision-making in hospitals and especially in hospital units that require high vigilance such as the Intensive Care Unit (ICU), is a dynamic and complex process requiring sound medical knowledge from appropriate information sources (pre-and post-pandemic). Quick and accurate decisions may be the difference in preventing clinical deterioration or managing life-threatening events in the ICU. The EMR was put in place to aid such a decision-making process. It functions as a one-stop knowledge-based system that consolidates reliable patient data, credible physician notes, and up-to-date nursing care charts, besides current hospital practice on similar cases.

Although studies have found that computerized entries by specialists or physicians may initially slow down physician productivity during the transition period (Noraziani et al. 2013), this was not captured in our findings. Instead, physicians find that their productivity increased with the use of EMR. It can be accounted for by the heavy usage and familiarity with electronic devices pre- and post-pandemic. All these accounts for the potential advantages of implementing the EMR.

Habit was a moderator pre-pandemic, but not post-pandemic. Given the time-lapse between pre-pandemic and post-pandemic data, the introduction of the EMR could have altered habits across time. However, we could not verify within this study, thus allowing the opportunity for extended studies. Another limitation is that this study focuses on physician productivity and is less oriented towards how the improved service quality of physicians after EMR adoption could impact their patients pre- and post-pandemic.

5.6 Conclusion

This study presents findings that investigate the relationship between physician technology perception on using the EMR (perceived usefulness and perceived ease of use, dynamic capabilities enhancement for knowledge acquisition and deployment) and physician productivity in the Intensive Care Unit (ICU) pre and post-pandemic. This study contributes to the broader discussion on the role of dynamic capabilities in the healthcare sector. As healthcare service organizations migrate to the use of the EMR to digitize patient data and health records, physicians could make better decisions based on consolidated patient information. As Malaysia continues its efforts to flatten the COVID-19 pandemic curve, we are learning more and more about the systemic manifestations of the disease among the critically ill. The EMR has enabled physicians involved in the ICU care of COVID-19 patients to adopt a multidisciplinary approach with ease. In response to the UN’s Sustainable Development Goal for “Good health and wellbeing,” our study offers evidence of rapid leverage on the information technology platform to enhance good decision making and patient care in a developing country.

Future research could extend the current model to examine the use of big data resources embedded in the EMR for effective decision making in critical care medicine. Given that the EMR implementation requires significant investment, future studies could determine the rate of return on investment, presently unexplored in this study. Furthermore, future studies may also explore the role of shared decision-making as a potential model for life-threatening diseases for patients in the ICU, as well as focus on how healthcare organizations could improve their physicians’ EMR adoption towards patient care.