There are many benefits of nursing professionals being able to consult and record electronic clinical histories (ECH) at the point of care. It promotes quality and patient security, communication, continuity of care and time dedicated to documentation.
This project evaluates the impact of having nursing records on electronic tablets at the patient’s bedside in internal medicine units in relation to the time dedicated to the records which could be used for patient care instead.
An analytical, longitudinal, prospective and experimental study with cross trial was carried out in the internal medicine units of a regional hospital from February to April 2017. The sampling was non-probabilistic and consecutive. The exclusion criteria were applied to the 23 candidates for the trial, and 13 nurses were selected. Each participant was observed 5 times in the control group (current system) and 5 times (tablets) in the experimental group. A total of 130 observations of two to three hours duration were made. We calculated the time dedicated to measuring key patient signs, patient evaluation and ECH recording. The variables were; group, shift, nursing code, age, sex, exclusion criteria, number of patients admitted, initial/final perception of the project, total time and the time spent per patient.
The analysis results for the whole sample show significant differences 0.44±0.13 min (w=-3.208, p=0.001) in the time dedicated to each patient. We then determined whether these differences were maintained in the night and afternoon groups. The latter was the group which demonstrated a significantly different reduction in time between the use or not of the tablets 0.60±0.15 min (T=3.82, p=0.01).
Except on the night shift, the findings showed a reduction in time spent on records when the tablets were used because transcription, latency time and displacements were no longer necessary. There were different results for the various work shifts, but we were unable to ascertain the causes or motives for this. It could have been due to multiple factors that can develop in any care situation in complex organisations like hospitals. These findings could be dovetailed and complemented with more detail by carrying out the quantitative part of the study.
Nurses are the largest group of healthcare professionals who use electronic records. They use them as a primary tool for documenting, synthesizing and communicating patient data. Therefore, introducing electronic devices in the different work areas has a great impact on this group (Rojas & Seckman, 2014).
It is essential that nurses are involved and committed to using electronic records. The use of electronic devices should be guided by nurses, as it is common for nursing professionals not to participate in their use (Rojas & Seckman, 2014; Gregory & Buckner, 2014; Kent, et al., 2015; Elgin & Bergero, 2015). Electronic records should be usable for nurses and relevant (useful) for their practice (Rojas & Seckman, 2014; Smallheer, 2015).
Concepts such as usability, utility, efficiency in the context of the users' use and trust in technology are key requirements for nurses to accept this innovation (Smallheer, 2015). Therefore, we need to know the contributions or opportunities that these devices offer as well as their limitations.
In the literature, authors frequently highlight the opportunity offered by electronic records for developing new instruments that improve the quality and efficiency of care, such as standardized care plans, checklists, and decision support systems. Standardized care plans also prioritize the need to make the profession visible by offering results that demonstrate the effectiveness of care (Rojas and Seckman, 2014; Kent, et al., 2015; Elgin & Bergero, 2015; Fernandez & Cibanal, 2009; Sousa, et al., 2012; Kelley, et al., 2011; Dykes et al., 2007; Medeiros, et al., 2014; Wager, et al., 2010).
However, some authors state that the devices can act as a limiting agent for care, reducing critical thinking, clinical judgment and basic nursing skills (Kent Redley, et al., 2015; Kelley et al., 2011; Sockolow, et al., 2014; Cornell, et al., 2010).
Another category that is important for the profession is saving time and communication. Some authors argue that electronic records decrease the time spent recording, because they have facilitating instruments such as copy and paste, or drop-down menus with content standardization. They also promote access to information, thus improving inter- and multidisciplinary communication, and consequently improving continuity of care (Fernández & Cibanal, 2009; Sousa, et al., 2012; Kelley, et al., 2011; Medeiros, et al., 2014; Cornell, et al., 2010; Smallheer, 2015).
Nevertheless, limiting agents in this category are also of equal importance. There is a greater volume of data available from any device and finding information relevant to healthcare practice is not always easy. The user is forced to navigate the entire system, opening multiple screens, or using different software, with duplicated information, to obtain an overview of the patient's condition. The user is also required to enter the "password" repeatedly for the different programs and/or the software has low performance, an excess of mandatory information, frequent interruptions, computers at the workstations are constantly busy, unavailable or shared, or with unfavourable ergonomics that deter nurses from trying to obtain information at the person's point of care. Another time-consuming cause, which authors have found, is a high percentage of transcription on paper of patient data, resulting in delays in patient information arriving to other professionals, and increasing the possibility of error. This is experienced by nurses as work overload, a time limiter and a barrier to communication. As a result, disruptions occur in the workflows, usability, and functionality of the program. A poor or poorly developed system can lead to interruptions in workflows and unnecessary delays in patient care. It increases the time for recording information and reduces the time of direct care to the patient (Gregory & Buckner, 2014; Kent Redley, et al., 2015, 2017; Fernandez & Cibanal, 2009; Kelley et al., 2011; Dykes et al, 2007; Medeiros, et al., 2014; Wager, et al., 2010; Sockolow, et al., 2014; Cornell, et al, 2010; Gocsik & Barton, 2014; Smallheer, 2015; Lee & Mcelmurry, 2010; Young, et al., 2012).
Some authors have proposed to improve these barriers by developing portable systems so that records can be made at the person's point of care (Wager, et al., 2010; Young, et al., 2012).
Bedside records promote patient quality and safety, communication, continuity of care, and workflows. However, nurses have varying perceptions and opinions.
There are portable systems that allow making patient-side records, reading barcodes for administering medication and identifying patients for accessing different screens or applications. These features increase patient safety. In addition, this system reduces errors from data transcription and latency time (information is recorded at the time it is obtained), which results in more accurate records. Quality is improved and relying on remembering information is avoided. It provides accurate, real-time information, which improves accuracy and therefore patient safety. Resources that do not facilitate bedside records make it easier to make errors. For example: a nurse would not know if the biobiotic prophylaxis by surgical intervention is correct if the electronic medical history is not registered (Calero & González, 2014; Wager, et al., 2010; Sockolow, et al., 2014; Fleischmann & Duhm, 2015; Thompson, 2005; Stuttle, 2009; Khole-Ersher, et al., 2012).
The possibility to access information within patient rooms facilitates communication between professionals, with the patients themselves and with their families, making them part of their health process and thus increasing their satisfaction and continuity of care (Smallheer, 2015; Sockolow, et al., 2014; Thompson, 2005; Stuttle, 2009; Khole-Ersher, et al., 2012; Carlson, et al., 2010; Blake, 2013). It decreases travel time, avoids transcription, and improves workflows, which all save time as it reduces the time spent recording and increases the time spent with patients (Calero & González, 2014; Stuttle, 2009; Carlson, et al., 2010; Blake, 2013).
However, nurses themselves have perceptions and opinions for and against bedside records. Authors have found that nurses are more satisfied with mobile devices and prefer to use them for complex patients (because they require a large amount of information), and for recording vital signs and blood products (Calero & González, 2014; Sockolow, et al., 2014; Stuttle, 2009).
However, they prefer not to use bedside records in two specific situations: for non-complicated patients, because their records are simple (require little data), and to admit the patient, because it takes a long time (it is better to find a quiet place to do so). Other reasons include that while they are recording the information they have to answer the questions of patients and families (disrupting concentration), they have the feeling that they are not giving good patient care (because they are concentrating on the screen) and that it does not offer them the opportunity to disconnect (the post-record gives them time and space for this). In addition, authors also argue that documentation is not a high priority in the nurses’ activities, or something that needs to be done straight away. Professionals do not feel that documentation affects the timeliness of patient care (Lee & Mcelmurry, 2010; Khole-Ersher, et al., 2012; Blake, 2013; Haller, et al., 2009).
In other words, for nurses, bedside patient records require mental and technical skills. However, they do not perceive that if they do not make the records, they will impair the quality of the care provided.
The literature pointed to a variety of devices and there is no consensus on the most appropriate resource for making bedside records. Evaluating this is complex, and is related to many different factors in each centre, such as the Wi-Fi connection and the access and identification system (Fleischmann & Duhm, 2015; Thompson, 2005; Stuttle, 2009; Khole-Ersher, et al., 2012; Carlson, et al, 2010).
In the literature consulted, no scientific articles were found in the Spanish state that evaluated the effectiveness of a practical experience with patient bedside records.
From other information sources there are two examples of practical experiences implemented in the Spanish state: the project of the Hospital Infanta Cristina de Madrid and Osakidetza Hospital in the Basque Country (Comunidad de Madrid, 2016; Departamento de salud de Euskadi, 2016, El Correo, 2017).
All these arguments show that it is important for nurses to decrease recording and technology time as it is seen as a complementary, administrative and bureaucratic task.
Nurses may perceive introducing technology as an increase in these bureaucratic tasks which may be a detriment to direct patient care. This situation may involve less physical contact with the patient and more time spent with electronic devices (Sockolow, et al., 2014).
One of the nurse theories that can help understand this is Dr. Ray's Theory of Bureaucratic Care, which focuses on nursing in complex organizations, such as hospitals. He explains that if we rely only on administrative theories or on theories focused solely on the patient-nurse relationship, the organization will not be able to adapt to new needs. Economic benefits and competitiveness (bureaucracy) prevail in contemporary organizations. However, there has been a resurgence of nursing as an art of science focused on human care (patient nurse relationship). The Bureaucratic Care Theory clarifies the meaning of human care in complex organizations, placing it at the centre as it is an essential part of hospital management. Human care self-organizes, interrelates, interconnects with each of its parts, placing spiritual-ethical care at the centre (the engine that moves nursing practice) and around it the bureaucratic factors, such as the educational, physical, sociocultural, legal, economic, political and technological factors (Coffman, 2011; Turkel, & Marilyn, 2007; Ray, 1989).
It is necessary to consider all these arguments and reflections when implementing any changes to electronic records. We believe that making bedside records with tablets can meet nurses’ needs and expectations. It can strike a balance between nurses’ need to provide direct patient care and the requirement to make records, and thus improve and facilitate care.
Recording the patient's clinical history at their bedside with tablets reduces the time spent on nursing staff records compared to the current record system in internal medicine units of reginal hospital.
To assess the impact of nurses making records at the patient's bedside with tablets in internal medicine units with respect to the time spent on records.
1. Describe the sociodemographic variables (age and gender) of the nursing professionals of internal medicine units participating in the study.
2. Determine the distribution of sociodemographic variables of the nursing professionals of internal medicine units participating in the study, depending on the shift.
3. Analyse the relationship of the sociodemographic variables of the participants in the study and the shift with the initial and final perception of the project.
4. Describe the sociodemographic variables (age and gender), shift, exclusion criterion and initial perception of nurses working in internal medicine units that were not able to participate in the study.
5. Assess the relationship between age and the exclusion criteria for nurses who were not able to participate in this study.
6. Evaluate the relationship of age and shift with the initial perception of the project of nurses who were not able to participate in this study.
7. Compare the difference between the age groups of participants and those not participating in the study.
8. Assess whether the implementation of this new record system decreases the time spent with patients compared to the current system.
9. Assess whether the implementation of this new record system decreases the time spent with patients compared to the current system in relation to the shift.
An analytical, longitudinal, prospective, experimental and cross-trial study was conducted for three months (February-April 2017) in the internal medicine units of a regional hospital. The sampling was non-probabilistic and consecutive. All participants who met the inclusion criteria in the study period were included. A representative sample of all reginal hospital nurses was ruled out because the working conditions did not allow a larger sample to be monitored. It was therefore decided to focus on a single unit that would make research feasible in time and resources. As the sample size was small, it was decided to establish a minimum amount of observations for each subject in the control and experimental group. The significance level was 0.05 and the beta risk was less than 0.2 in a bilateral contrast. The common standard deviation was 1.96, and therefore the size would be 15 observations per control group and 15 observations for the experimental group to detect a difference equal to or greater than 1.5 units, estimating a monitoring loss rate of 10%.
Following these calculations, it was decided to make a minimum of 10 observations of each nurse, five for the control group (current system) and five for the experimental group (using the tablet). The total of 23 professionals could participate in this trial for three months, as they were the only ones who had the minimum dedication time to be able to be observed continuously; however considering the inclusion criteria, there were 13 individuals who could participate satisfactorily. Each person could only be in one group.
A time interval called a "round" was observed, consisting of the routine established at the beginning of the afternoon and evening shift when constants are taken, the nurse activities are standardized, and an overall assessment is made of the patients. It is also the time to record the data in the health records. The same was done for both groups. The control group used the medication trolley with the laptop (current system) and the experimental group used the tablet. There is no such routine in the morning on this unit.
The inclusion criteria were as follows:
• The nurse is a regular employee on the unit. A minimum of 10 observations can be made in three months, as this would facilitate the adaptation and learning curve in the experimental phase.
• The nurse carries out care activity on the internal medicine unit. This is because it is a robust unit, in which the occupancy rate is more stable and the average hospital stay is greater than three days (Pérez-Martí, 2018).
• The nurse works the afternoon or night shift. The night shifts are longer than the other shifts because the staffing and distribution of activities is different (Marrugat & Vila, 2012).
• Voluntarily participation in the study
• The nurse has a certain level of competence according to Benner's skills acquisition model: it is necessary that the nurse has worked more than three years in the hospital, because in this time they can learn the functioning of the hospital and the Clinical History, the protocols and procedures of the centre (Benner, 1984).
• The nurse can use Information and Communication Technologies (ICT) (Haller, et al., 2009)
The tools for gathering information were two ad hoc databases with study variables. Structural variables were defined such as group, shift, nurse code, age, sex, exclusion criteria, number of patients admitted, initial/final project perception, total time and time spent per patient.
The total time was the result of the sum of the "round" time and the time spent making the health records whatever the resource used. The time spent per patient was obtained by dividing the total time by the number of patients admitted. In addition, it was identified as the main variable of the study, because it is more standardized.
The selection of variables is justified by a study conducted at a Toronto hospital that measured the time nurses spent making records. The variables studied were the total time, the time spent per patient and the number of patients admitted. This study compared paper records versus electronic records (Young, et al., 2012).
Other studies have used the variables sex, age, nationality and previous experience related to positive perception of electronic nursing records (Kelley et al, 2011).
The rest of the variables were chosen to adapt the research to the study field and to the practical and real situation of the work units.
A statistical analysis was carried out with SPSS 17.00 (Statistical Package for Social Sciences) and a significance level of p <0.05 was applied. For categorical variables, the frequency calculation and a contingency table were performed using The Likelihood Ratio analysis (n<60). For the quantitative variables, for each of the groups, the Shapiro-Wilks normality test (n<30) was performed. The mean, standard deviation and variance were analysed with ANOVA, and two-factor averages for samples (control and experimental group) were contrasted with the Student T test (Wilconson test when the sample was out of normality). Following this analysis, when the results showed differences in averages between groups, Cohen's D was applied to measure the distance or effect between the groups.
This paper is part of the research of a doctoral thesis with a favourable report from the Bellvitge Clinical Research Ethics Committee. The privacy and confidentiality of informants have been respected.
During the data collection period, a total of 179.50 observation hours were carried out in the afternoon hours from 15h to 17.30h and at night from 22h to 01h. Each observation lasted between two to three hours. Of the 23 professionals working in the internal medicine unit with stability, 13 (56.52%) were able to participate in study, and 10 (43.47%) could not.
The sociodemographic characteristics of the study participants were: 15.3% (n=2) are men and 84.5% (n=11) women, with an average age of 38.08 (±1.40) years, in women 38.55 (±1.63) years and in men 35.50 (±0.50). The two groups were homogeneous in the variables studied, there are no statistically significant differences in age between sexes and shifts (sex Leven p-0.04, M-W p-0.37; shift Leven p-0.02 M-W p-0.1).
There was a positive initial perception in 46.2% (n=6), negative 30.8% (n=4) and neutral 23.1% (3) and a positive final perception of 69.2% (n=9), negative 7.7% (n=1) and neutral 23.1% (n=3) for the total sample (n=13). Initial and final perceptions about the project after participating in the project did not vary statistically in shift or age variables. However, in the group of only women (n=11) the results varied because the women's final perceptions improved after they participated in the study. The initial positive perception was 45.5% (5) and final positive perception was 63.9% (7) with significant differences (Verisimilitude Ratio P=0.031) with a Substantial Cramer Coefficient (v=0.65).
The sociodemographic characteristics of the non-study participants (n=10) were 10% (1) men and 90% (9) women, with an average age of 42 years ( ±4, 3) years. 50% (5) worked the afternoon shift and the other 50% (5) on the night shift. The exclusion criteria obtained were experience <3 years with 30% (3), work leave with 20% (2), unable to use Information and Communication Technologies (ICT) at the basic level with 20% (2), and change from the unit of internal medicine to emergency services 10% (1). The initial perception of non-participants was positive at 60% (6), negative at 30% (3) and neutral at 10% (1).
If we look at the relationship of these variables to the initial perception towards the project, we can see that there is homogeneity with age and that this relationship with the exclusion criteria cannot be established because the data are not robust. However, a statistically significant relationship is obtained with the shift variable (Verosimilitude Ratio P=0,04), the positive perceptions of non-participants towards the project are better in the afternoon shift (80% positive ) than at night (40%) with a very strong coefficient of association (V of Cramer p=0.97). After the study had been explained, non-participating subjects of the night shift had a worse perception or acceptance.
As shown in Table 1 and 2, statistically significant differences in age are evident between the group of non-participants (excluding experience groups <3 years) and the group of the afternoon shift participants and night shift participants. The "Non-participating" average age was 49.57 (±2, 92) years compared to the "afternoon shift participants" with an average age of 37.71(±2, 33) years old and the "night shift participants" aged 38.50 (±1, 60) years (Tukey afternoon p=0, 007; Tukey night p=0, 014).
Table 1: Average comparison (ANOVA) of age variable
Table 2: Multiple comparisons of averages between groups
The quantitative variables were used to measure and compare the times required to carry out the "round" and document the health records between the control group (current system) and the experimental group (tablet). To carry out the cross trial, 13 nurses participated and a sample of 130 observations was obtained.
Of the total sample, the mean of the total time obtained for the control group was 55.44 ± 2.11 min and 11.77 ± 0.25 patients admitted. For the tablet group, an average total time of 48.30 ± 2.24 min and 11.37 ± 0.28 patients admitted was observed.
Comparing the time spent per patient (the main variable of the study) it is evident that the average of the time spent per patient is lower with the tablet group (= 4.22 ± 0.14 min) than the control group (= 4, 66 ± 0, 12 min), there are statistically significant differences (W=3, 208, p=0, 001) and a low effect (D=0,44) between groups (Table 3).
Table 3: Average comparison time spent per patient
However, if we focus on analysing these variables considering the shift worked, the results bring a nuance or specificity to this more general data. The number of patients admitted is homogeneous and similar in the control (12.63± 0.22 patients) and tablet (12.50± 0.39 patients) groups. There are no statistically significant differences in their distribution (W afternoon p=0.22; W night p=0.96).
The averages of the total afternoon shift time of the control group was 44.83 ± 2.21 min and tablet group was 35.48 ± 1.17 min. The control group's night shift was 67.83 ± 2.22 min and the tablet group was 63.27 ± 2.78 min. The comparison of average of the factor for related samples shows that there are significant differences in the afternoon shift (T= 4.07, p=0.00), with a high effect between groups (D=0.93), but not on the night shift (T=1.29, p=0.2).
In the afternoon shift for the control group, the time spent per patient was an average of 4.07 ± 0.13 min and in the tablet group it was 3.47 ± 0.10 min. In the night shift the control group had an average of 5.36 ± 0.13min and the tablet group had an average of 5.09 ± 0.19 min. Comparison of the averages of a factor for related samples shows that, in the afternoon shift, the mean of the time spent per patient was lower with the tablet group, with statistically significant differences (T=3.82, p=0.01) and there was a high effect (D=0,77) between groups. However, the same results are not obtained on the night shift (T=1.16, p=0.25) (Table 4).
Table 4: Average comparative total time, number of patients admitted, and time per patient per shift
This research presents results on the impact of implementing a resource change involving nurses making records at the patient's bedside on tablets. This brings new and relevant data to the relatively little-studied research subject.
The relationship between the age, sex and shift variables with the initial and final perception, after having participated in the project, showed that there are no statistically significant differences in the variables studied, except in the group of women. The women's perceptions of the project improved after participating in the study.
Of the non-participants, there were statistically significant differences for the shift and age variables. This group is older than the group of participants and initial perceptions of the project were better in the afternoon shift than at night.
Only one article was found in the literature that studied the relationship between sociodemographic variables and the nurses’ perceptions and attitudes towards electronic records. The authors stressed that the study of these variables improves knowledge of workflows. There are other articles with contradictory and inconclusive results concerning the relationship between age and sex and a better or worse perception. They only found consistent data related to previous experience in the use of computers with more favourable attitudes towards electronic records (Kelley et al., 2011).
The main variable of our study was the time spent per patient. Statistically significant differences were evident (p <0.05) for both the total sample and the afternoon shift group. For the total sample the time saved using the tablets was 0.44 ± 0.13 minutes with a low effect and for the afternoon shift it was 0.60 ± 0.15 min with a high effect. However, in the night shift, although there are differences in time, they were not significant.
Therefore, we can accept the study hypothesis, where making records at the point of care with tablets decreased the time spent on documentation compared with the current record system in internal medicine units, for the total sample and the afternoon shift group of nurses. However, we cannot reject the null hypothesis for the night group.
The literature consulted showed conflicting results that discussed whether electronic records reduced the time spent on records by citing numerous benefits and limiting agents. However, there is a consensus on the concept time: for nurses’ time is an important and present concept. During their working day they carry out numerous activities always keeping in mind the way of organizing these so they can do them all in their work shift. Nursing is a pragmatic profession, in which an activity must have a certain result. However, it is also a profession of contact and relationship with the patient through providing care, which is the main axis and motivation of their profession. Therefore, saving time in bureaucratic activities, to have more time for human care is a constant concern (Gregory & Buckner, 2014; Kent Redley, et al., 2015; Smallheer, 2015; Fernandez & Cibanal, 2009; Sousa, et al., 2012; Kelley et al., 2011; Dykes et al, 2007; Medeiros, et al., 2014; Wager, et al., 2010; Sockolow, et al., 2014; Cornell, et al., 2010; Coffman, 2011; Turkel & Ray, 2007; Ray, 1989).
A study conducted before this test (pre-situation), carried out in the same service and hospital (same study field) used a work system established for years and therefore known and integrated into the usual practice, but with different methods according to the nurse. One system was to transport a medical trolley with a laptop; and the second system was to write on paper and then later type it into a computer. It was shown that there were no significant differences in the working method used and the time spent per patient. However, the participant's subjective perception was contradictory. The nurses argued that the reason for choosing the work system was because they had the perception that they saved time, although this was not quantitatively true. However, the nurses all agreed that neither of the systems met their expectations, both were slow and cumbersome, and made it difficult to access information and records. Nevertheless, statistically significant differences in shift work were evident, although the motives were unclear. The time spent per patient on the night shift was 1.33 min significantly higher than the afternoon shift. It was therefore concluded that another work system had to be implemented, but it was considered that the shift was an important variable to consider in future research (Pérez-Martí, 2018).
Therefore, the statistical analysis of the research results was carried out for the whole sample, but also for these two categories. It is evident that the results are positive in the afternoon shift but that this is not so in the night shift. This corroborates that it is important to take into account the variable shift work in the analysis and that this change of resource in the night shift is not enough to improve the documentation time and that there are probably other causes that could influence the results.
This approach is in accordance with the results obtained by other authors who explained that nurse/patient workflows are established in a complex, dynamic, variable context, with continuous relationships and interactions (multifactorial and multidimensional), typical of a social experience in a nursing team (Gregory & Buckner, 2014; Kent, et al., 2015; Cornell, et al., 2010; Lee & Mcelmurry, 2010). Moreover, nursing care develops and is influenced by the social or cultural structure of the organization (Ray, 1989).
The total number of patients admitted from the two shifts was homogeneous, 11.77 ± 0.25 in the control group, and 11.37 ± 0.28 in the tablet group. The distribution was also similar between the groups of the two shifts.
Only five articles were found in the literature consulted that assessed the impact of the patient's vital signs, although they use different research methods and variables. Only two of these refer to the number of patients admitted. The first discussed the implementation of an electronic system using four different resources, including tablets, indicating a ratio of six to eight patients admitted. The second, using electronic records and paper, with a ratio of four to six patients admitted and a total time of 65.2 min and time spent per patient of 13.04 min with electronic records (Wager, 2010; Young, et al., 2012).
Comparing the results shows that the unit of study ratio is double, and the times are shorter than the other two studies. It should be noted that one study was conducted in Canada and the other in the United States. The differences in working ratios between countries are extremely different, and therefore the working conditions differ as well. However, these authors highlighted time differences with electronic records that caused an increase in latency and transcription errors (Wager, 2010; Young, et al., 2012).
Other authors of published articles related to point-of-care records reached similar conclusions. It takes less time to make the records because there is no need to move to a different place to make the record and work is halved because there is no need to enter the hand written record into the computer later (Calero & González, 2014; Stuttle, 2009; Carlson, et al., 2010; Blake, 2013).
Previous studies and this research have a common cause. Using tablets decreases the time spent on making records because there is no need to make transcriptions or move to another place to make the record and latency time is avoided.
The time the researcher dedicated to the study was limited because it was carried out outside of working hours, there were few resources available to carry out the research (one tablet) and there were huge difficulties in making 10 observations with the same participant. For these reasons, there are limitations in terms of sample size and non-probabilistic sampling type. To ensure external validity, this research could be repeated with a larger sample with random sampling in the future.
However, no greater control has been taken over the confusion variables that could cause the results to vary. This would improve internal validity.
Finally, the number of nursing activities recorded per patient (constants, pain, catheter, oxygen therapy, health education, scales, etc.) was not quantified at the time of observations. This was to avoid the effect of the observer and to promote informal acceptance of the study by the participants. The cost benefit of implementing these measures would have to be assessed for future studies.
Women had a better perception of using digital devices to make patient records after participating in the study. Non-participants on the night shift had the worst perception of the project and this group tended to be older in age.
The age difference and initial perception could be related to fear of change or the unknown, to previous experiences with technology and their ICT skills. These factors can make people reluctant to participate in a technological project. Having to use a new resource or device can make people feel insecure and nervous, which could be accentuated at an older age due to their experience with unsatisfactory digital changes and/or they are not an ICT user at the domestic level.
However, more studies with a larger sample would be needed to improve the validity of the variables perception, age and previous experience in ICT, with both participants and non-participants to complement these indications, which had a statistically significant relationship.
The quantitative data showed that the use of tablets decreased the time spent making records for the entire sample and for the participants of the afternoon shift; however, the same results were not obtained for the night shift. It was not possible to determine the factors in the two shifts that led to the different results. They may be due to a multifactorial event, like any care situation in complex organizations such as hospitals.
Therefore, all related factors would have to be assessed, both technological and those that depend on the context, as well as the other factors that can affect the project. It is essential to understand the initial situation and the impact that the change will make on workflows in the study field and how this will relate to and be influenced by the context in order to anticipate the possible obstacles. The difficulties can then be overcome as far as possible and their actual impact understood. That is, it is necessary to be able to determine whether the implementation of a new resource, such as tablets, has been beneficial for nursing professionals in general and in all its nuances and peculiarities. Otherwise we could make statements that are wrong or biased that do not correspond to reality and that do not solve the problem.
It could also be analysed whether the device used is the most suitable or if there are other more appropriate tools, as well any software and technical barriers should be assessed.
Concerning the other factors, it should be considered that the care the nursing professionals provided is part of a context, of a nursing team that works and interacts with other professionals, which cares for numerous patients with different needs and idiosyncrasies, where numerous and varied activities are carried out in a quick and changeable way, where professionals have different aptitudes, skills, perceptions and beliefs, and in which there is a large number of protocols and procedures, and that nurses are in contact and have a relationship with the patient, families, other professionals, the hospital as an institution, and other health care centres. These findings could be triangulated and supplemented in more detail when the qualitative part of this project is elaborated.
The effort this will entail is justified because having electronic records at the point of patient care reduces movements and avoids the transcription of data, making them more reliable, decreasing latency time and improving patient safety, record quality and communication between professionals.
At a time when technology is part of our society and considering that nurses are important players in hospital work flows, it is imperative to go into depth in similar studies that provide more information and allow us to develop systems that promote better patient care.
The data used in this article have been obtained in the thesis entitled: “Evaluation of implanting a point-of-care record system with tablets in a local hospital” directed by Dr. Lina Cristina Casadó-Marín and Dr. José Oriol Romaní-Alfonso. We are also grateful to Arnau Besora (Pere Virgili Fundation) and Dr. Francesc Valls (Universitat Rovira i Virgili -URV) for their support in the statistical analysis. We would like to thank URV language service and Chris Place for his English revision of the text. Finally, we would like to thank the nurses of the internal medicine unit for their collaboration.
The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
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Alpert, B., Quinn, D., Gallick, D. (2014). Oscillometric blood pressure: A review for clinicians. Journal of the American Society of Hypertension, 8, 930–938 (2014). https://doi.org/10.1016/j.jash.2014.08.014
Boutes, A., Brackman, L., Martin, E., Subasic, M., & Potkanswicz, E. (2018). The accuracy and validity of iOS-based heart rate apps during moderate to high intensity exercise. International Journal of Exercise Science, 11 (7), 533-540. https://digitalcommons.wku.edu/ijes/vol11/iss7/4
Burnham, J. P., Lu, C., Yaeger, L. H., Bailey, T. C., & Kollef, M. H. (2018). Using wearable technology to predict health outcomes: A literature review. Journal of the American Medical Informatics Association: JAMIA, 25(9), 1221-1227. https://doi.org/10.1093/jamia/ocy082
Cadmus-Bertram, L. (2017). Using fitness trackers in clinical research: What nurse practitioners need to know. The Journal for Nurse Practitioners, 13(1), 34-40. https://doi.org/10.1016/j.nurpra.2016.10.012
Carroll, J., Moorhead, A., Bond, R., LeBlanc, W., Petrella, R., & Fiscella. (2017). Who uses mobile phone health apps and does use matter? A secondary data analytics approach. Journal of Medical Internet Research, 19(4): e125. https://doi.org/10.2196/jmir.5604: 10.2196/jmir.5604
Chandrasekhar, A., Kim, C.-S., Naji, M., Natarajan, K., Hahn, J.-O., & Mukkamala, R. (2018). Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Science Translational Medicine, 10(431), eaap8674. https://doi.org/10.1126/scitranslmed.aap8674
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (9), 319-340. https://doi.org/10.2307/249008
ECRI Institute. (2019). 2019 Top 10 Patient Safety Concerns. Executive Brief. https://www.ecri.org/landing-top-10-patient-safety-concerns-2019
Evenson, K., Goto, M., & Furberg, R. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12, 159 – 181. https://doi.org/10.1186/s12966-015-0314-1
Jamaladin, H. J. H., Belt, T. H. v. d., Luijpers, L. C. H., Graaff, F. R. d., Bredie, S. J. H., Roeleveld, N., & Gelder, M. M. H. J. v. (2018). Mobile apps for blood pressure monitoring: Systematic search in app stores and content analysis. JMIR mHealth and uHealth, 6(11), e187. https://doi.org/10.2196/mhealth.9888
Lim, W. K., Davila, S., Teo, J. X., Yang, C., Pua, C. J., Blöcker, C., Lim, J., Ching, J., Yap, J., Tan, S., Sahlén, A., Chin, C., The, B., Rozen, S., Cook, S., Yeo, K., & Tan, P. (2018). Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research. PLoS Biology, 16(2), e2004285. https://doi.org/10.1371/journal.pbio.2004285
Low, C. A., Bovbjerg, D. H., Ahrendt, S., Choudry, M. H., Holtzman, M., Jones, H. L., Pingpank, J., Ramalingam, L., Zeh, H., Zureikat,A., & Bartlett, D. (2018). Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission. Annals of Behavioral Medicine, 52(1), 88–92. https://doi.org/10.1093/abm/kax022.
Luo, H. , Yang, D. , Barszczyk, A. , Vempala, N. , Wei, J. , Wu, S. J. , Zheng, P. P. , Fu, G. , Lee, K. & Feng, Z. (2019). Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. Circulation: Cardiovascular Imaging, 12(8), e008857. doi: 10.1161/CIRCIMAGING.119.008857.
Marston, H., Freeman, S., & Musselwhite, C. (Eds). (2017). Mobile e-Health. Springer International Publishing.
McCarthy, J. (December 11, 2019). One in Five U.S. Adults Use Health Apps, Wearable Trackers. Gallup Polls. https://news.gallup.com/poll/269096/one-five-adults-health-apps-wearable-trackers.aspx
Mlakar, M., Puddu, P. E., Somrak, M., Bonfiglio, S., Luštrek, M., Chiron and HeartMan research projects, & on behalf of the Chiron and HeartMan research projects. (2018). Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients. PloS One, 13(3), e0190323. https://doi.org/10.1371/journal.pone.0190323
Morrissey, E. C., Glynn, L. G., Casey, M., Walsh, J. C., & Molloy, G. J. (2018). New self-management technologies for the treatment of hypertension: General practitioners' perspectives. Family Practice, 35(3), 318-322. https://doi.org/10.1093/fampra/cmx100
Plante, T., Urrea, B., MacFarlane, Z., Blumenthal, R., Miller, E., Appel, L., & Martin, S. (2018). Validation of the instant blood pressure smartphone app. JAMA Internal Medicine, 176(5), 700–702. https://doi.org/10.1001/jamainternmed.2016.0157.
User Manual Blood Pressure Monitor EBP 095 (n.d). https://images-na.ssl-images-amazon.com/images/I/911kKv3naKL.pdf
Vogels, E. (January 9, 2020). About one-in-five Americans use a smart watch or fitness tracker. Pew Research. https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/
Lisa Anne Bove, DNP, RN- BC
Dr. Lisa Anne Bove has worked in healthcare informatics for over 25 years in a variety of positions and has been certified by the American Nurses Credentialing Center in nursing informatics since 1996. Her work has focused on improving health through the use of electronic information. Dr. Bove has been an interim chief nursing informatics officer of a major multi-system hospital system, program manager for large clinical design and implementation projects, and educator for both continuing education and college level classes. Her field of study is around ease of use and usefulness of mobile health care technology to advance practice through the use of data. Her teaching is focused on informatics, as well as on project management, EHR implementation and leadership.
Rachel Carroll, PhD
From Dr. Rachel Carroll’s training as a biostatistician in a public health department to her current role as a professor at a university charged with serving the region in which it is situated, community focus and engagement has always been a motivator for her research and study. Dr. Carroll has published over 30 peer-reviewed articles throughout the course of her career in high-impact journals. Topics have included statistics, biostatistics, spatial statistics, time series analysis, environmental research, oncological research, epidemiology, and social sciences.