Impact of a Disease-Focused Electronic Health Record Dashboard on Clinical Staff Efficiency in Previsit Patient Review in an Ambulatory Pulmonary Hypertension Care Clinic

Abstract

Objectives We aimed to improve the operational efficiency of clinical staff, including physicians and allied health professionals, in the previsit review of patients by implementing a disease-focused dashboard within the electronic health record system. The dashboard was tailored to the unique requirements of the clinic and patient population.

Methods A prospective quality improvement study was conducted at an accredited pulmonary hypertension (PH) clinic within a large academic center, staffed by two full time physicians and two allied health professionals. Physicians' review time before and after implementation of the PH dashboard was measured using activity log data derived from an EHR database. The review time for clinic staff was measured through direct observation, with review method—either conventional or newly implemented dashboard—randomly assigned.

Results Over the study period, the median number of patients reviewed by physicians per day increased slightly from 5.50 (interquartile range [IQR]: 1.35) before to 5.95 (IQR: 0.85) after the implementation of the PH dashboard (p = 0.535). The median review time for the physicians decreased with the use of the dashboard, from 7.0 minutes (IQR: 1.55) to 4.95 minutes (IQR: 1.35; p < 0.001). Based on the observed timing of 70 patient encounters among allied clinical staff, no significant difference was found for experienced members (4.65 minutes [IQR: 2.02] vs. 4.43 minutes [IQR: 0.69], p = 0.752), while inexperienced staff saw a significant reduction in review time after familiarization with the dashboard (5.06 minutes [IQR: 1.51] vs. 4.12 minutes [IQR: 1.99], p = 0.034). Subjective feedback highlighted the need for further optimization of the dashboard to align with the workflow of allied health staff to achieve similar efficiency benefits.

Conclusion A disease-focused dashboard significantly reduced physician previsit review time while that for clinic staff remained unchanged. Validation studies are necessary with our patient populations to explore further qualitative impacts on patient care efficiency and long-term benefits on workflow.

Keywords

electronic health records - disease-focused dashboards - previsit review - efficiency - workflow

Background and Significance

Electronic health records (EHRs) are standard throughout the world.[1] Although widespread use of EHRs has been linked to improved patient safety and quality of health care, there is mixed evidence for its benefits in terms of workflow efficiency, cost of care, and long-term clinical outcomes such as mortality.[2] [3] [4] [5]

Efficient data management within EHRs remains a challenge across medical specialties.[6] Locating and integrating relevant information, interfaces that do not match clinical workflow, information overload, and poor usability are some of the frequently reported difficulties that hinder optimal use of EHRs in clinical settings.[7] [8] [9] The suboptimal design and poor usability of EHR may lead to decreased efficiency, alert fatigue, delayed responses, higher job dissatisfaction, and burnout among health care workers potentially compromising patient care.[10] [11] [12]

Studies have shown that a significant portion of clinical time is spent in the EHR, which otherwise would have been given to direct patient care.[13] [14] The problem is compounded by the ever-increasing amount of information stored in EHRs. Obtaining clinical information from the most current EHR systems requires users to perform complex navigations through multiple screens, tabs, and pages including long multi-step processes. This phenomenon in human–computer interaction is described as the “keyhole effect,” which refers to the difficulty of accessing a large amount of information through a small viewing space, similar to peering into a large room through a keyhole.[15] Such complex navigations in EHRs have been linked with reduced clinical efficiency, increased cognitive load, alert fatigue, and incidences of medical errors.[16] [17] [18] [19] [20]

The theory of distributed cognition offers a framework to understand the challenges posed by current EHR designs and suggests that cognition extends beyond individual minds and encompasses people, tools, and the environment.[21] Furthermore, this framework acknowledges that humans possess limited cognitive resources, including perception, attention, and memory. Cognitive load, described as the strain on working memory caused by tasks, systems, or environments, is increased when there is an overload of information to process, necessitating manual integration from multiple sources, or when the placement of information does not match the workflow.[22] [23] [24] By externalizing resources, such as through well-designed visual displays, the burden on working memory to retain information can be significantly alleviated, allowing for more resources to be allocated to higher cognitive functions like diagnostic reasoning and problem solving.[25]

Emerging as potential solutions to address the challenge of efficient data organization, visualization, and retrieval, EHR dashboards consolidate and streamline information scattered throughout patient records onto a single screen.[7] [26] [27] [28] Disease-focused dashboards, tailored for quick access to pertinent patient information in the collated form, hold promise for enhanced workflow efficiency, particularly in highly specialized clinical areas.[20] [29] However, there is a notable dearth of evidence regarding the potential impact of clinical dashboards, particularly disease-focused ones, on enhancing workflow efficiency in real-world clinical scenarios. While previous studies have shown reductions in overall patient encounter time, improved adherence to guideline-based treatment, as well as decreased EHR review time following dashboard implementation, many of these studies were conducted under simulated patient scenarios.[27] [30] They did not fully consider the complexities of the real-world clinical environment, where workflow efficiency can be affected by factors such as the complexity of a case, interruptions, distractions, technology malfunctions, and other human-factor-related variables. Furthermore, very few published studies have evaluated the impact of the dashboard on previsit patient review time although other metrics such as documentation time, order entry time, prescription time, etc. have often been reported.[20] [26] [27] [31] [32] [33] [34]

The previsit patient review is crucial for efficient and effective consultations, but retrieving data from EHRs for this purpose can be time-consuming.[35] [36] In the context of a pulmonary hypertension (PH) ambulatory clinic, there are typically a few core laboratory, physiologic, clinical, and radiological parameters that are routinely evaluated in each visit to determine the severity of the disease and clinical status among the patients with an established PH.[37] [38] The clinical staff, both physician and allied health professionals at the PH clinic of our center, frequently reported in EHR improvement surveys that the data necessary for these patients require navigation through multiple EHR screens taking too much time, needing to remember information between the screens, often requiring revisiting the same screens. To address these challenges, we conceptualized and implemented a customized, disease-focused dashboard within our existing EHR system (Epic Systems, Verona, Wisconsin, United States). This dashboard aimed to streamline data accessibility by consolidating all pertinent patient information into a single screen, thereby facilitating comprehensive follow-up care of patients with PH. By providing easy access to organized patient data, the dashboard was anticipated to enhance workflow efficiency and contribute to the overall quality of patient care in our PH clinic.

The current study aimed to investigate the impact of integrating this customized dashboard into the EHR system on the workflow efficiency of physicians and allied health professionals for previsit patient review in an ambulatory PH clinic. The primary objective was to assess whether the implementation of this dashboard significantly enhanced the time efficiency compared with the conventional chart review method to collect key clinical data for a previsit patient review.

Methods

Study Design and Setting

The design is that of a prospective quality improvement study that employed a comparative observational approach to evaluate the impact on workflow efficiency of a disease-focused dashboard integrated into the EHR system and followed the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guideline. The study was conducted exclusively within the PH outpatient clinic at Mayo Clinic Florida, United States. This PH clinic is an accredited center through the Pulmonary Hypertension Association and is associated with a tertiary medical center. This center is composed of two full-time physicians who primarily see PH patients and two allied health staff (one registered nurse [RN] and one respiratory therapist [RT]), who see approximately 1,700 patients yearly. This clinic has been using an EHR since the early 2000s and Epic since 2018. The typical workflow of our PH clinic is summarized in [Supplementary Fig. S1] and [Supplementary Table S1] (available in the online version)

Fig. 1 Pulmonary hypertension dashboard within Epic EHR system. EHR, electronic health record.

The new disease-focused dashboard for the PH clinic was developed and implemented into the clinical workflow in June 2023. The current study was performed from July 1, 2023 to December 31, 2023, which allowed the clinical staff to use the new dashboard for over a month before testing. Physicians' EHR use-related data were obtained from August 2022 to November 2023 (a total of 16 months: 10 months of conventional use and 6 months with the dashboard) from the EHR metadata platform, Epic Signal.

Dashboard Development

The development of the dashboard was driven by a user-centric design approach, facilitated by a multidisciplinary team of physicians, information technologists, and end users, particularly clinicians from the PH clinic. This collaborative effort involved identifying key data elements based on user needs (clinician and staff members) and national guidelines (expert knowledge).[37] [38] Through iterative design refinements and continuous feedback loops with end users, the dashboard evolved to balance comprehensiveness and usability, aimed at streamlining clinical workflows and reducing record search time. Over 6 weeks and four design meetings, the team iteratively refined the dashboard's design, ultimately finalizing it ([Fig. 1]). Additionally, heuristic evaluation was employed to systematically enhance usability, further refining the dashboard's effectiveness in supporting clinical workflows.

The data that were selected for the final dashboard included patient demographics (name, date of birth, and contact information), patient care coordination notes, current problem list, central venous catheter history, relevant consult notes (rheumatology, lung transplant, heart transplant, liver transplant, and cardiology), research study participation, immunization history, prognostic risk score employing Registry to Evaluate Early and Long-term pulmonary arterial hypertension disease management (REVEAL) Lite 2 severity score with breakdown, modified New York Heart Association/World Health Organization functional class, laboratory work (blood natriuretic peptide, N-terminal pro b-type natriuretic peptide, complete blood count with differential, hepatic function, chemistry group, antinuclear antibody, anti-centromere antibodies, anticyclic citrullinated peptide antibodies, human immunodeficiency virus antibody, and thyroid stimulating hormone level), 6-minute walk distance, submaximal exercise test results, echocardiogram results, chest X-ray report, computed tomography (CT) chest results, ventilation perfusion scan, overnight oximetry, pulmonary function test, and right heart catheterization. All testing that could be personally reviewed would display a link that would then display that result. For example, for CT chest imaging, there would be a link that, when opened, would retrieve the image to review. All results would display the current result and previous results. If the laboratory test was never performed, then its value would not appear on the dashboard.

Throughout the study, the conventional method was defined as manually searching the patient's chart to obtain the data necessary for each person's role prior to a patient's encounter. The dashboard method was defined as utilizing the newly developed and implemented disease-focused dashboard to obtain the data necessary for a patient encounter.

Participant Selection

Physicians' previsit review time was collected from the EHR metadata platform, Epic Signal which tracks time in review before each visit and then gives a mean time over a given time period. The time period selected for this study was monthly, as this was shortest time period available. A postdoctoral research fellow (RF; male) was also studied to act as an allied health staff member. As they had no experience using either of the review methods, they would have no usage bias, providing a balanced perspective on the efficacy of the review methods. No patient were was collected for this study.

Data Capture and Analysis

Physicians' previsit review time was collected from the EHR metadata platform, Epic Signal which tracks time in review before each visit and then gives a mean time over a period, which monthly was chosen for this study. We also recorded the total number of patient visits attended by each physician during the same period to determine the average number of patients reviewed per day. The review time was defined as time spent reviewing test results and patient history.

Additionally, from the same metadata, we calculated physician-level per-visit averages for the “Note time,” “Total EHR time,” and “After-hours EHR” time as secondary metrics of EHR use. To compute these, we divided the total monthly active time spent by physicians on these specified EHR activities by the number of monthly patient visits. Note time was defined as the time spent documenting the clinical encounters. Total EHR time was defined as total active time spent in the EHR per visit. Time spent outside scheduled hours (after-hours time) was defined as the time spent in the EHR system 30 minutes before or after the physician's first and final visits for the day. This after-hours time was collected from days the physicians had scheduled patients and days when there were no scheduled patients or unscheduled days. We also collected comparable data from 1 year before the implementation of the dashboard for the conventional method.

At our center, before each visit, allied health staff also review the patient's chart and fill out a worksheet (referred to as PH worksheet hereafter) with specific data elements essential for the visit ([Supplementary Fig. S2], available in the online version). This worksheet is printed out and given to the physician and patient for the visit, along with being faxed to the patient's insurance company. This worksheet is essential for obtaining insurance prior authorizations for medications and for updating the patient on their medical condition.

Fig. 2 Changes in physicians' EHR metrics relative to daily patient load before and after the dashboard implementation. Only metrics that exhibited statistically significant changes are shown here. The black solid arrow marks the month of implementation of the dashboard. The asterisk (*) represent after-hours EHR time on unscheduled days. EHR, electronic health record.

Allied health staff were directly monitored in real-time to measure their previsit review time, which encompassed reviewing the patient's chart and completing the PH worksheet, during regular clinic hours. Each staff member was timed using a stopwatch, beginning when the staff opened the patient's chart and concluding upon the completion of the worksheet (indicated by sending the print order). Each staff reviewed 20 patients, 10 with each of the conventional and dashboard method, overseen by TK₁, a postdoctoral RF with over 7 years of research experience. Since Epic Signal data were not available for allied health staff within our EHR, we manually recorded the task completion time. To prevent bias, the assignment of review methods (conventional vs. dashboard) was randomly allocated using online random number generator. The timing of the allied health staff was performed following 2 months of using the dashboard in clinical practice. The RF was timed by another fellow (NG₂) using the same method described before, filling out the same PH worksheet for 10 patients in phase 1 (six via conventional and four via dashboard method) (Piloting) and 20 patients (10 with each method) in phase 2 (follow-up), which was conducted after they have exposure with the workflow for more than 6 months. The direct continuous observation approach was employed for time measurement in the current study, which is considered the gold standard.[34] [39]

Adverse outcomes were defined as any errors in the data collected for the worksheet identified by any of the health care staff.

Physicians and allied health staff provided subjective feedback each month during the study period and upon the study's conclusion, which were recorded as direct verbatims. We utilized content analysis to summarize our qualitative data.

Statistical analysis was conducted utilizing IBM SPSS Statistics version 28. The Shapiro–Wilk test was used to assess the frequency distribution for normality and it was determined to be nonnormal. Median (interquartile range [IQR]) comparisons for previsit review time, note time, total EHR time, and after-hours EHR time using each method (conventional and dashboard) were assessed using the Mann–Whitney U test. Inter-user discrepancies within each category of clinic staff were assessed using the Mann–Whitney U test or Kruskal–Wallis test as appropriate. Additionally, we conducted a subgroup analysis among allied health staff, categorizing them as experienced or inexperienced based on their exposure to the PH dashboard. All tests were two-tailed with a significance level set at p <0.05.

Results

EHR Use Metrics among Physicians

We extracted 16 months of data from the Epic Signal metadata on physicians' EHR usage at our PH clinic. Before the implementation of the PH dashboard, physicians saw a median of 5.50 patients per day (IQR: 1.35). Following implementation, this number increased slightly to 5.95 patients per day (IQR: 0.85), although the change did not reach statistical significance (p = 0.535; [Table 1]).

Table 1 Previsit review time and daily patient load by review methods and staff sub-categories among physicians

Staff category	Daily patient load (visits per provider)				Mann–Whitney U-test, p-value	Review time (minutes per visit)				Mann–Whitney U-test, p-value
	Conventional		Dashboard			Conventional		Dashboard
	Median	IQR	Median	IQR		Median	IQR	Median	IQR
Physicians
Overall	5.50	1.35	5.95	0.85	130.0, 0.535	7.00	1.55	4.95	1.35	13.5, <0.001[a]
P1	5.85	1.47	5.70	0.68	29.5, 0.958	7.60	3.38	3.95	0.33	4.0, 0.003[a]
P2	5.40	0.60	6.05	0.60	36.0, 0.328	6.80	0.40	5.25	0.18	2.5, 0.002[a]

Abbreviations: IQR, interquartile range; P1, Physician 1; P2, Physician 2.

^a Statistically significant.

The median previsit review time, our primary EHR use metric, for the physicians was 7.0 minutes (IQR: 1.55) which decreased significantly to 4.95 minutes (IQR: 1.35) following the implementation of the PH dashboard (p < 0.001; [Table 1]).

In addition to previsit review time, we assessed four other EHR use metrics: notes time, total EHR time, after-hours time on scheduled days, and after-hours time on unscheduled days ([Table 2]). Total EHR time per visit and the time spent outside clinic hours on unscheduled days dropped significantly from 15.15 minutes (IQR: 3.37) and 30 minutes (IQR: 11.50), respectively, to 12.21 minutes (IQR: 2.91) and 17 minutes (IQR: 4.75) during our study period (p = 0.035 and p < 0.001, respectively; [Table 2] and [Fig. 2]).

Table 2 Additional EHR metrics by review methods among physicians

Parameters	Time frame				Mann–Whitney U-test, p-value
	Before dashboard implementation		After dashboard implementation
	Median	IQR	Median	IQR
Notes time per visit	4.15	1.78	2.95	1.58	68.0, 0.064
Total EHR time per visit	15.15	3.37	12.21	2.91	62.0, 0.035[a]
After-hours time on scheduled days	16.00	6.20	10.00	8.08	72.5, 0.093
After-hours time on unscheduled days	30.00	11.50	17.00	4.75	28.0, <0.001[a]

Abbreviation: EHR, electronic health record; IQR, interquartile range; SD, standard deviation.

^a Statistically significant.

EHR Use Metrics among Allied Health Staff

We prospectively recorded 70 PH patient encounters reviewed by the allied health staff with randomly assigned review methods. The review time among these categories of staff referred to the time to review all the data elements as performed by physicians and the completion of the PH worksheet ([Supplementary Fig. S1], available ein the online version). The median review time for the group of allied health staff including a RF was 4.65 minutes (IQR: 2.05) with the dashboard and 4.43 minutes (IQR: 0.72) with the conventional method (p = 0.752). Segregated analysis showed no significant change in median review time with the introduction of the PH dashboard among experienced clinic staff (RN and RT) as well as inexperienced users (RF) in the piloting phase ([Table 3]). Notably, the median review time was lower for the more experienced support staff than with the postdoctoral RF. However, in the follow-up study (phase 2), after the RF became familiar with the clinic workflow for more than 6 months, the review time significantly decreased with the dashboard compared with the conventional method (3.59 minutes [IQR: 0.88] vs. 4.84 minutes [IQR: 0.42], respectively, p = 0.004; [Table 3]).

Table 3 Previsit review time by review methods and staff subcategories among allied health staff

Staff category	Conventional			Dashboard			Mann–Whitney U-test, p-value
	No. of patients reviewed	Review time (minutes per visit)		No. of patients reviewed	Review time (minutes per visit)
		Median	IQR		Median	IQR
Allied health staff
Overall	20	4.43	0.69	20	4.65	2.02	222.0, 0.565
RN	10	4.83	0.87	10	4.93	1.80	59.5, 0.481
RT	10	4.38	0.38	10	4.24	1.56	50.0, 1.000
Research fellow
Overall	16	5.06	1.51	14	4.12	1.99	61.0, 0.034[a]
RF (phase 1)	6	7.25	1.54	4	6.61	0.50	9.0, 0.610
RF (phase 2)	10	4.84	0.42	10	3.59	0.88	13.0, 0.004[a]

Abbreviations: RF, research fellow; RN, registered nurse; RT, respiratory therapist; SD, standard deviation.

^a Statistically significant.

[Fig. 3] is a box and whisker plot showing comparative review time for physicians and clinic staff by review methods (conventional vs. dashboard). Only physicians were found to have significant improvement in time efficiency after the implementation of the PH dashboard (p < 0.001).

Fig. 3 Pr-visit review time by user categories and review methods.

There were no adverse outcomes, defined as any errors in the data collected for the worksheet, with either of the review methods studied.

Subjective Feedback

Physicians reported positive feedback, citing the ease of accessing patient data. However, allied health staff expressed mixed views. While they found the display and placement of data elements efficient for the review, they experienced some difficulty with the workflow, particularly in completing the worksheet.

“…we had to access the worksheet within the EHR system, and unfortunately, it couldn't be popped out to view separately. Consequently, we were constantly toggling between dashboard screens to collect data and then returning to the worksheet page to input it.” (Allied health staff)

Discussion

The current study aimed to evaluate the impact of implementing a disease-focused dashboard within the existing EHR system on the workflow efficiency of clinic staff, particularly for the previsit patient review at an ambulatory PH clinic. The result showed a significant reduction in median review time among physicians (4.95 vs. 7.0 minutes, p ≤ 0.001), but no significant change among allied health staff (4.65 vs. 4.43 minutes, p = 0.752) with the use of PH dashboard compared with the conventional approach. These findings highlight the potential of disease-focused dashboards to enhance workflow efficiency for physicians but suggest the need for further optimization tailored to the specific requirement of allied health staff.

The majority of current clinical dashboards are being used as a tool for clinical decision support (CDS).[40] [41] Numerous studies have highlighted their efficacy in enhancing patient care across various domains. For instance, they have proven beneficial in improving cardiovascular risk assessment in primary care,[42] increasing medication adherence in conditions like rheumatoid arthritis,[43] to even improving compliance with critical care protocols to mitigate iatrogenic conditions such as ventilator-associated pneumonia in surgical intensive care units[44] and reducing inaccuracies and factual errors during handoff.[20]

While benefits in terms of improved patient care are well-documented, the impact of dashboard on actual clinical workflow efficiency has not been extensively studied. Dagliati et al reported reduction in overall visit duration in addition to increased screening compliance for the complications of type 2 diabetes with the use of a dashboard-based system.[45] Similarly, Fadel et al assessed the utility of using a visual dashboard in addition to conventional EHRs. They demonstrated reduced encounter time and increased adherence to guideline-based treatment of essential hypertension compared with using EHRs alone among simulated patients in an ambulatory clinic.[30] Koopman et al reported significant time efficiency and accuracy in data retrieval for high-quality diabetes care in a similar setup using an EHR dashboard compared with the conventional method.[27] Significant reductions in total EHR time after the implementation of PH dashboard found in our study are in agreement with these prior studies.

Much like our study, Laing and Mercer observed a notable reduction in chart review time for preventive care decision making in a Canadian family practice, without compromising accuracy. This was achieved using a CDS tool that automatically summarized patients' histories and preventive screening results.[46] Their study further extrapolated that implementing this tool could save their center 82.6 hours of clinic time annually. A cluster-randomized trial from Taiwan reports significant reductions in the time required to gather key clinical data for prerounding in intensive care unit settings with the implementation of new visualization dashboards compared with established EHRs.[26] Beyond highlighting improved workflow efficiency, this study also underscores significant enhancements in the accuracy of clinical information exchange and the formulation of effective recommendations. A similar study in primary care settings at the Mayo Clinic also reported significant reductions in the time spent deciding on preventive services and chronic disease management.[47] Additionally, Nelson et al provided valuable insights into the operational benefits of dashboards in the anesthesiology department, showing a marked reduction in the number of clicks and mouse scrolls required to review patients' prior anesthetic records.[48]

In the current study, we observed a marked decrease in after-hours EHR usage post-intervention, a metric seldom studied in previous research.[49] This metric is notoriously challenging to define and measure, as it varies depending on EHR vendors.[50] As reported in one study, increased after-hours time spent on the EHR is associated with burnout and less work–life satisfaction among providers in primary care.[51] PH patients require extensive clinical care and are frequently admitted to the hospital just on the disease process alone.[52] As this clinic is run by two physicians and is located within a tertiary medical center, these physicians frequently are called on their unscheduled days to review and provide recommendations to PH patients. In the current study, we found significant reduction in after-hours EHR use following dashboard implementation. Our findings suggest that dashboard tools could alleviate after-hours work for physicians, potentially enhancing efficiency and reducing burnouts.

It is important to note that most of the studies discussed above were conducted using simulated patient scenarios.[20] [27] [30] [46] While this approach allows for controlled variables and standardized, repeatable experiments, it does not fully capture the complexities of real-world clinical settings. In actual practice, factors such as case variability, interruptions, distractions, technology malfunctions, and other human-related issues are prevalent. The current study, which utilized longitudinal data from real-world patient encounters, provides a more comprehensive understanding of how clinical dashboards perform in actual clinical environments.

Interestingly, while experienced allied health staff members did not display significant changes in review time indicating no short-term efficiency benefits of the dashboard, their less experienced counterparts experienced a notable reduction after becoming acquainted with the dashboard. This observation suggests the potential for efficiency gains with increased familiarity and proficiency, a phenomenon commonly reported among less experienced trainees such as resident physicians.[53] Nonetheless, it is important to note that the workflow efficiency facilitated by the dashboard, even if not quantitatively significant, might still contribute qualitatively to staff productivity and overall workflow optimization, patient safety, and quality of care.[20] [26] [27] [31]

On subjective feedback, physician had mostly positive response endorsing the utility of PH dashboard while allied health staff had mixed responses primarily related to workflow challenges stemming from a limitation in the EHR system. Specifically, the inability to view the worksheet separately forced staff to continuously switch between screens or tabs to gather data, resulting in operational complexities. This likely contributed to the absence of time differences between review methods among them. Efforts to address this issue are ongoing for future implementation. Similar workflow-related concerns were expressed by the physician for a visual dashboard designed for identifying risk of multiple hospital-acquired conditions.[54]

The current study focused on a specialty medical practice; however, the approach and insights derived from this dashboard-assisted chart review hold relevance across diverse clinical settings. The dashboard data elements can be fine-tuned to suit the unique needs of specific patient populations, along with multiple dashboards that the physician can select depending on the clinical context for each encounter. Furthermore, dashboards designed for specific preventive care holds significant promise for improving long-term management of chronic diseases. Prior studies provide robust evidence supporting the effectiveness of preventive care–focused dashboards. For instance, the integration of specific diabetes-related metrics into a dashboard facilitates the streamlined review of tests, medications, and due procedures, thereby facilitating informed decision-making in preventive care interventions.[27] [45] [47]

Limitation

Several limitations of this study should be taken into consideration. The small sample size and specific clinic setting might have constrained the generalizability of the findings. Additionally, the relatively short implementation period of the dashboard could have limited the depth of understanding regarding its long-term efficacy and impact. Furthermore, the users' varying levels of background technical proficiency and potential learning curves associated with the adoption of the new dashboard could have influenced the results. Complex clinical presentations and possibility of observer bias while directly monitoring clinic staff during the study could also have affected the outcomes.

However, the promotional value of this study is considerable due to its execution in a small, specialized clinic where all members were actively involved in both the development and implementation phases. This level of engagement likely influenced the high percentage of usage of the dashboard method observed in the study. Nonetheless, it is important to acknowledge that such a controlled environment may limit the generalizability of the study findings to larger and less-controlled settings. This underscores the significance of obtaining buy-in from the majority, if not all, staff members to effectively implement improvements in a broader scale.

Conclusion

In conclusion, the current study demonstrated a significant reduction physicians review time, total EHR time, and time spent outside of clinic hours with the implementation of a disease-focused dashboard at our PH clinic. Among allied clinical staff, experienced members showed no significant difference in review time, while inexperienced staff demonstrated a notable reduction after becoming familiar with the dashboard, suggesting its potential in efficiency gains. Further studies exploring the qualitative impact on the overall patient care experience and long-term workflow benefits would be valuable for a comprehensive assessment.

Clinical Relevance Statement

The results of this study support the benefits of integrating disease-focused dashboard in EHRs to increase health care worker's efficiency in previsit patient review thereby improving the clinical workflow, direct patient interaction, and overall safety and quality of care.

Multiple-Choice Questions

1. What was the primary objective behind implementing the disease-focused dashboard in the electronic health record system for the pulmonary hypertension clinic?

   • To increase the complexity of patient encounters during previsit patient review.

   • To assess the overall quality of patient care in the clinic.

   • To facilitate efficient data accessibility and streamline previsit patient review.

   • To reduce the cognitive load on health care workers during patient consultations.

   Correct Answer: The correct answer is option c. To facilitate efficient data accessibility and streamline previsit patient review.

   Explanation: The primary objective of implementing the disease-focused dashboard in the electronic health record system for the pulmonary hypertension clinic was to facilitate efficient data accessibility and streamline previsit patient review. By consolidating pertinent patient information into a single screen, the dashboard aimed to enhance workflow efficiency and contribute to the overall quality of patient care in the clinic.

2. What insight does the study provide regarding the impact of disease-focused dashboards on after-hours EHR usage?

   • After-hours EHR usage increased among physicians.

   • After-hours EHR usage decreased significantly across all staff members.

   • After-hours EHR usage was not affected by the implementation of the dashboard.

   • After-hours EHR usage decreased among physicians.

   Correct Answer: The correct answer is option d. After-hours EHR usage decreased among physicians. We observed a marked decrease in after-hours EHR usage among physician postintervention. This finding suggests that the implementation of disease-focused dashboards could alleviate after-hours work for health care professionals, potentially enhancing efficiency and reducing burnout.

Conflict of Interest

None declared.

Acknowledgments

The authors would like to acknowledge the Pulmonary Hypertension Clinic staff John Moss, Tonya Zeiger, and Lauren La Moureaux for their valued participation in the study.

Protection of Human and Animal Subjects

This study was deemed exempt from Institutional Review Board review for quality improvement purposes.

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Publication History

Received: 27 March 2024

Accepted: 14 August 2024

Article published online:
06 November 2024