Why use emrs




















Questions, comments or want to check-in? Phone Email. Skip to content Sales: Support: x 2 Request Demo. Benefits of EMR for the Medical Practice — From the viewpoint of doctors and health practitioners there are numerous other advantages of implementing electronic medical records: The ability to quickly transfer patient data from one department to the next is a huge asset The space saving benefit of a digital records environment The ability to ultimately increase the number of patients served per day for enhanced patient workflow and increased productivity Improved results management and patient care with a reduction in errors within your medical practice Reduced operational costs such as transcription services and overtime labor expenses Customizable and scalable electronic medical records that can grow with your practice Advanced e-Prescribing and clinical documentation capabilities Plus an improved bottom line of the healthcare practice, enhanced through the ability to more accurately and efficiently process patient billing.

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But opting out of some of these cookies may affect your browsing experience. Necessary Necessary. These clinical summaries consist of information about the care provided during the visit, medications prescribed, upcoming appointments, and other medical advice. Patients can quickly receive electronic copies of all their healthcare information upon request through EMR. Suppose they decide to change their healthcare providers; In that case, the universal electronic medical record will help give their new healthcare provider a complete understanding of their medical history and current state of health.

Picking the right EMR system can be difficult as most EHR systems currently available do not come in a one-size-fits-all format. So it is crucial to look at the types of EMR software available.

EMRs can help maintain and improve your quality of care. However, transitioning from a physical system to EHRs may be difficult, there are steps in electronic medical record training you can take to become competent and confident in your use of EMRs. When you make the leap to EMRs, remember to keep your care patient-centered instead of computer-centered.

Focus on your patient, give them undivided attention and make eye contact. Doing this will help take the stress of EMR transition away from you as you focus on the patient and help you make an accurate diagnosis. Also, continue interacting with them while you enter data into their record and encourage them to participate in the setting up of their electronic medical record.

This facilitates patient engagement and increases their satisfaction with the encounter. It is better to separate routine data entry from your time with the patient to improve efficiency and optimize patient satisfaction.

Similarly, you can enter data after your patient has explained their concerns, allowing them to drive the flow of information while you direct content to clinically relevant topics. Practitioners can do this as you improve your computer and typing skills and efficiency.

Another way to do this is by working with templates in your EMR software since they can significantly reduce the time required in data entry during patient visits. This blog post is throughly an example of how medical emr systems is important for both physicians and patients. EMR should be patient centered than just an ordinary data entry work. It enables estimating associations of latent variables unobserved variables, measured with multiple items and estimating multiple regression equations, including more than one dependent variable in one model.

The results show the level of fit between the observed items and the latent variables. Regression parameters express the strength of the association between the latent variables. Each measure has to meet a particular threshold to indicate a good fit. The model that we built to test our hypotheses consists of four parts: Organisational aspects, Success of implementation, Decision of user and Result of using the EMR Fig.

The social system that needs to adopt the innovation [ 8 ] is the hospital and the hospital contains a number of elements affecting the implementation process [ 36 , 37 ]. In the following sections, we present the elements of the empirical model and literature of implementation research related to the subsequent elements.

The management of a hospital has several tools to guide organisational processes. Authentic leadership Fig. Bottom-up influence is related to leadership and is known to affect the support of EMRs [ 16 , 17 , 41 — 43 ].

The model includes these two management tools. The precise wordings of the items are presented in the Additional file 1. An other organisational aspect that we included in the model is support from other departments.

Starting to work with an EMR requires new skills that need specific education and training during the implementation stage [ 44 ]. The HR department may help identifying these needs and offer targeted courses and training [ 21 ], thus improving the performance of the clinicians working with the EMR [ 45 ]. IT support may help to overcome practical problems. Hence, support from the IT department may play an important role during and after the implementation stage [ 46 ].

And because of the intrinsic link between EMR and administration, having administrative staff that supports users with entering patient data is likely to affect the successful implementation and use of an EMR [ 19 ].

The model includes three constructs for support from the IT, HR and administrative department. See the Additional file 1 for precise wordings.

A culture that enhances communication [ 47 ], or a culture that is innovative [ 48 ] and open [ 21 , 42 ], is likely to contribute to the success of the implementation of EMR.

We included two measures of culture: open culture and innovative culture, based on work of Woerkom [ 49 ] and de Jong et al. The success of implementation is measured with two items: ease of use and alignment with daily routine [ 51 ]. This notion is based on the Technology Acceptance Model e. The result of the EMR implementation is measured with two variables: ease of use and alignment with daily routine of the user groups [ 51 ]. When the added value of good quality patient data becomes clear to the user, the satisfaction with the system as well as its use are likely to increase [ 53 ].

We assume that the fit between technology, user and task affects the use of the innovation [ 5 ] in the sense that a better fit between daily routine and ease of use is likely to further the use of the innovation. We expect that ease of use and alignment of EMR to the daily routine will positively affect the perceived added value of EMR and that the users consequently will enter the patient data more timely in the EMR.

The ultimate dependent variable, presented in Fig. Then the users will take full advantage of the potential of the EMR and it will support the organisation in process management, e. Three factors are expected to influence quality of the data. First, by the way in which patient data are entered into the EMR Fig. We assumed that the quality of the data is higher when the time between seeing the patient and entering the patient data is shorter. Second, when the EMR is easy to use, and this may be the result of a well-functioning implementation process, the quality of the data is higher.

Third, a better alignment of the EMR functionalities and the daily routine of its users [ 14 ] is also expected to positively affect the quality of the data.

The measurement of level of implementation is based on answers of respondents about computerisation of EMR in their hospital is next to using paper files.

The score is higher for respondents working with a completely computerised EMR. The groups doctors and nurses are compared in two ways. First, we investigate whether the doctors and nurses differ in the regression parameters in differing from zero. Second, we compare significant differences of the regression coefficients between the groups by re-estimating the multi group model, but constraining the regression parameters to be similar across groups.

Subsequently, we ran models in which the parameter of interest was allowed to differ. Subsequently, the chi 2 change of the two models is compared. If the chi 2 improves significantly, we conclude that the doctors and nurses differ in behaviour on this regression parameter.

The differences in chi 2 and subsequent significance levels are reported in the right hand columns of Table 2. Only complete questionnaires were included. Due to item non-response, respondents were included in the analyses doctors and nurses.

Where possible, open answers were used to add to the closed questions, e. Sample characteristics are shown in Table 1. In the Netherlands, there are 85 hospitals, 8 of which are academic hospitals 9.

Footnote 1 This means that in our respondent group, nurses and doctors from academic hospitals are overrepresented. This can be explained by the fact that we intended to include only nurses and doctors who had experience with working with EMR and that Dutch academic hospitals are at the forefront of implementing EMR. Table 2 and Fig. Table 2 shows the regression coefficients of the multi group model for the two groups doctors and nurses and the analyses of the difference between the two groups.

In the right columns, the chi 2 difference with one degree of freedom is presented of a model with constraint regression parameters and a model with that particular regression parameter allowed to differ between the groups.

If the difference is significant, this means that the nurses and doctors differ on this parameter. Figure 2a presents the SEM model for nurses and Fig. Figure 2c presents the differences between the two groups. The solid arrows represent the significant regression paths and the dotted arrow the non-significant results. The solid arrow represent the paths of which the doctors and nurses differ see table 3 far right column , dotted arrow represent paths with similar results for doctors and nurses.

Regression coefficients reported in B and s. The doctors and nurses do not differ in these aspects. Concerning the control variables, we see that doctors and nurses react differently to the level of implementation. This study aimed to increase our understanding of which organisational factors affect the success of implementation processes and how this may differ between doctors and nurses.

The body of literature is considerable and many competing theories and models exist. In many theories and models, the organisation, the innovation or technology and the user [ 1 , 5 — 9 ] play a role. In many theories and models the purpose of an implementation process, i. A new element of our study is that we applied the model to two different user groups doctors and nurses and found that they responded differently in a number of ways. Differences between stakeholders in implementation processes are acknowledged previously, but as far as we know, this is the first study to show that different user groups react differently to the same organisational aspects in an implementation process.

The most prominent differences between the doctors and the nurses are in their estimations of the influence of organisational support on implementation success. We find that the doctors and nurses differ in reaction to the support of the HR department. While the nurses tend to value HR support negatively, the doctors appear to state that HR has little influence.

The results of bottom-up communication are positive as expected for both user groups and when the EMRs are easier to use and better aligned with the work of the users, the added value is perceived to be higher.

We expected to find that clinicians who saw fewer benefits from working with the EMR would be more inclined to delay entering the patient data. However, organisational aspects were found to restrict much of the opportunities of the clinicians to hamper the use of the EMR.

The characteristics of the result of the implementation are more closely associated with the use of the EMR. We did not find this for the nurses. In this study, an innovative culture implies that colleagues and managers are inclined to consider new ideas to improve work processes. Interestingly, we found that an open culture, which is open to discussions on suboptimal performance, did not result in a better outcome of the implementation process.

Contrary to expectations, nurses who had more opportunities to discuss problems with colleagues and management also found the EMR harder to work with. An explanation might be that in an innovative culture, the employees are encouraged to try new things, and not to dwell on what went wrong due to new working methods. It should also be noted that the data used to investigate the measurement of the theoretical constructs relies on the perceptions of members of the organisation as they are reported by members of the organisation.

Although such information is insightful, for example because organisational members have more confidence in projects if they believe that the management of their organisation can manage them appropriately, it should be acknowledged that these perceptions may not fully capture the actual situation in an organisation. This can be seen as a limitation, but may also be argued that the perceived situations such as culture but also quality of the data is what really affects the work processes of the doctors and nurses, more than the objective situation.

If management for instance reports to ensure sufficient bottom-up communication, but doctors and nurses experience differently, measuring this parameter at the managerial level is not likely to enhance our understanding of the mechanisms that affect the use of innovations. Measurements of the factors in the model may be improved by combined views of more stakeholders and study where views overlap.

Measurement of the quality of the data of EMRs can be measured even better since the files are stored and can be retrieved at a later moment in time. Therefore, improved measurements of the quality of data are for instance to retrospectively rate consistency and completeness of the patient files. This study shows that different actors react differently to the same organisational factors and future studies may focus on differences and similarities of perceptions of organisational factors and how these affect implementation processes.

We constructed the models based on the theoretical consideration presented in the introduction. First, we constructed the scales based on factor analyses. Next we built the model, assuming that organisational factors would affect the result of the implementation process and that in turn this would affect the response of the users and subsequent quality of use of the innovation.

We took all organisational factors as a starting point without hypothesising on possible causation among these factors.

It could also be argued for instance that leadership and bottom-up communication are enablers for the quality of practical support of the HR, IT and administrative departments. This could then maybe provide a mechanism to explain the effects of leadership and bottom up communication.

However, given that both aspects also appear to have direct relations with the variables that indicate the success of the implementation, we assume that this model is sufficiently close to reality without having to add to the complexity of the model. Given the relatively low response and the overrepresentation of respondents working in academic hospitals, the analyses may be biased to a certain extent.

In general, academic centres are first to adopt novel techniques and people working in academic hospitals may be more inclined to use innovations than doctors and nurses in non-academic hospitals.

By implication, in particular the variance in the attitudinal measure is lower than it would have been if the sample were representative on this parameter. Part of the lack of significance of the attitudinal variable may be that the respondents can do little else but to adopt an innovation that is implemented organisation-wide.

Doctors and nurses differ in a number of aspects in their response to new use of EMRs. Bottom-up influence gave the most coherent results: for both doctors and nurses, the success of the innovation came with more bottom-up communication.

A second relatively consistent finding is that support of the IT departments yields a positive result on EMR implementation.

Doctors and nurses differ in their responses to the support of other department. Organisational culture had some influence but it seems to be less important and may work as a negative factor in use of EMRs. By comparing the different factors that can influence the success of implementation, this study contributes to the scientific field of innovation and the implementation of new technology.

We found indications that characteristics of the EMR ease of use and alignment with tasks have more influence on the quality of the data in the EMR than timely entering of patient data by the user groups. For hospital managers, results of this study are directly applicable. Many of the actions they take, may result in differences in reactions from the user groups, sometimes in subtle but possibly relevant.

However, when the users have bottom-up influence during and after the implementation process and when the IT department has the skills and ability to give optimal support to the users, this may positively affect the implementation success.



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