Clinician Needs for Electronic Health Record Pediatric and Adolescent Weight Management Tools: A Mixed-Methods Study

Keywords

electronic health records - children - primary care - family medicine - workflow - health care provider - dashboard - mixed

Background and Significance

Rates of pediatric obesity in the United States continue to increase. In 2020, 20% of children and adolescents in the United States had obesity.[1] Pediatric primary care and weight management specialists can play an important role in trying to reduce these trends but are faced with competing demands, time constraints, and ineffective, insufficient tools to support their management of these patients.[2] [3]

Technology offers one option to support clinicians working to manage pediatric and adolescent obesity. Previous studies have shown that electronic health record (EHR) tools can assist with chronic medical management and prevention of chronic diseases[4] [5] [6] including pediatric obesity.[7] [8] [9] [10] (The American Academy of Pediatrics in partnership with Curbside Health offers an EHR tool that provides clinicians with patient individualized recommendations [labs, referrals] and resources [handouts]. https://downloads.aap.org/AAP/PDF/Obesity/About%20the%20CPG%20FHI R%20Pathway.pdf”.) Antiobesity medications offer new opportunities for weight loss in adolescents and adults; however, these medications are more effective when used in combination with lifestyle modifications.[11] [12] Health data either logged by patients in mHealth apps or tracked through wearable smart devices, such as Smart Watches, can be aggregated and integrated into the EHR. Gamified mHealth apps and patient-logged data to facilitate health behavior change and growth charts that document medication use can contribute to weight loss management. If these data and other clinic-generated data, such as office weights, blood pressure, and labs can be organized and displayed in a useful EHR tool or dashboard, it has the potential to improve the efficiency and effectiveness of weight management and primary care visits.

Unfortunately, many of the currently available integrated mHealth apps and EHR tools are not developed with sufficient clinician input.[13] Consequently, the design of these EHR tools did not address current barriers, informational needs, and clinic workflow issues needed to design EHR tools to help clinicians address complex medical issues. Suboptimal EHR tools risk further increasing distractions for clinicians or adding to clinic complexity and burden of EHR documentation. For the effective management of obesity using health information technology tools such as EHR, clinicians' buy-in is critical.[14] Previous studies have shown that health outcomes, including individual body mass index (BMI), as well as clinician performance, improve with the implementation of EHR tools.[15] Some of the barriers toward the successful adoption of such tools are alert fatigue, logistic technical issues, and lack of tailoring to clinicians' needs.[14] Clinicians also pointed out the need for EHR to have accessible educational materials on obesity management, including resources, local programs, and activities that can be tailored to individual needs.[16] This study attempts to address gaps in the literature by exploring clinicians' preferences and needs to design an effective EHR pediatric obesity tool.

Objectives

The purpose of this mixed-methods study was to understand clinicians' barriers to effective weight management counseling and to explore ways that EHR tools can assist clinicians to help overcome some of these barriers. If EHR tools and data visualizations are designed with clinician input and address current barriers to weight management care, they are more likely to be adopted by clinicians in weight management and preventative health visits.

Methods

A mixed-methods study was conducted to develop a comprehensive picture of clinicians' current informational needs and barriers when conducting a pediatric or adolescent weight management appointment and the best way to fulfill those needs. It was conducted as the preliminary part of a larger project to inform the development and iteration of an EHR weight management tool ([Fig. 1]) which displays patient-logged health behavior from CommitFit. CommitFit is a gamified mHealth app that asks users to set health behavior goals (fruit/vegetables, water, decreased sugary beverages, physical activity, and sleep) and then awards points to users for logging these behaviors and achieving their goals. The app then uses leaderboards and locked avatar gear to gamify these health behavior goals.[17] The EHR weight management tool was developed to allow clinicians to view CommitFit health behavior data and other weight-related information in a useful and efficient manner. Both the CommitFit app and EHR weight management tool were developed utilizing user-centered methods. An on-going larger study (pilot randomized controlled trial) is evaluating the efficacy of CommitFit to help adolescents and their caregivers achieve a healthy weight and maintain positive health behaviors.

Fig. 1 CommitFit EHR Weight Management tool provider facing prototype that displays health behavior data entered by patient in real time using CommitFit app. This can be shared with patients during clinic visit. This was developed through an iterative user-centered process based on feedback from the three focus groups (fictional data).

We used exploratory sequential design approach for mixed-methods integration. The study team first collected qualitative data and findings that informed subsequent quantitative data collection. The focus groups and questionnaires were conducted sequentially with results from the clinician focus group informing the survey questions and iterative development of the EHR weight management tool. We chose to conduct this study with mixed-methods approach as it can enhance the research by validating quantitative findings using qualitative data. Similarly, quantitative data can assist in explaining the findings from qualitative data. This can help develop a more robust understanding of current clinician practices and barriers to pediatric obesity management and to understand the role of EHR tools to overcome these barriers.

Focus Groups

We conducted three separate virtual focus groups with faculty in the Family and Community Medicine and Pediatric Departments at the University of Missouri—Columbia (MU). Residents were excluded from the focus groups. These 60-minute sessions occurred virtually over Zoom. We obtained informed consent from participants prior to the focus groups. Participants were encouraged to participate in all three focus groups to allow iterative development of the EHR weight management tool and were invited to complete the final questionnaire. The principal investigator (A.B.) and coinvestigators (A.T. and R.K.) moderated the focus groups using a semistructured script. For each focus group, a third and fourth investigator (P.G., E.M., K.T.B., or L.F.) observed and took field notes. The moderator encouraged balanced participation from all focus group members. Moderators started with broad and indirect questions about the topic before asking focal questions. Each participant answered these questions and was encouraged to interact with fellow participants to explore individual and group perspectives. This approach was used to limit socially desirable responses.

During the semistructured focus groups, moderators began by inquiring about clinicians' approaches to addressing child and adolescent weight in their clinics. Moderators asked participants about any barriers or facilitators to pediatric weight management or lifestyle counseling in their practice. The discussion then shifted to EHR tools for pediatric weight management, what features and data would be most useful, and how an EHR weight management tool would be most effectively integrated into a primary care or weight management specialty clinic.

In addition to taking notes during the focus groups, the researchers video recorded the focus groups and transcribed the audio recordings[18] using Microsoft 365. Both the original audio recordings and the transcribed data were carefully reviewed to ensure the accuracy of the data.[19] Anonymity of data helped control for social bias. Codes derived from the dataset that were similar were combined into broader themes (thematic analysis). Codes and themes were described and documented to assist in the review and examination of the data. This ensured themes derived from the analysis were consistent with the collected data. This resulted in a robust set of themes originating from the data and ensured that they fit well together. The themes were subsequently analyzed using Dedoose, a qualitative analysis tool. Independent thematic analysis was conducted by A.B., P.G., and K.T.B. Investigators met to reach a consensus on codes and final themes. A methodic approach including prolonged engagement with data, peer debriefing to review and discuss the findings, and transparency among team members helped avoid unexplained bias and increase credibility. Triangulation of data from focus groups and surveys increased the rigor and trustworthiness of the findings.

Questionnaire

A convenience sample of clinicians also completed a 38-item electronic questionnaire that included demographic questions, questions about confidence and effectiveness of managing obesity, current EHR tools to support weight management, and preferences for an EHR weight management tool ([Table 1]). The questionnaire was developed by clinicians on the research team with experience managing pediatric obesity and revised based on feedback from focus group participants. The questionnaire intentionally used neutral language was beta-tested by the research team and focus group participants prior to its full distribution. The electronic questionnaire link was first emailed to all MU family medicine and pediatric faculty members who participated in the focus groups and then those who did not participate in the focus group, utilizing Qualtrics. To increase the sample size, reach a larger geographic area to increase generalizability, and include both academic and nonacademic clinicians, the REDCap questionnaire link was then emailed to clinicians at two private and one academic pediatric clinic in Missouri, and finally, shared on the North American Primary Care Research Group (NAPCRG) researcher forum. REDCap was used for the latter database because it allows for the collection of social security numbers which was requested for participant compensation, outside of the MU system. Due to the small sample size, the questionnaire data were analyzed using descriptive statistics only. For scaled questions, we used a scale that ranged from 1 to 100, to increase question sensitivity. A mean >50 is considered favorable, <49 less favorable. This research was approved by the MU Health Sciences Institutional Review Board (#2054598).

Table 1 Clinician questionnaire. Selected questions from 38-item emailed clinician questionnaire

(2) Current practices

Rate these statements from 0 (strongly disagree) to 100 (strongly agree).

2.1: I have effective child/adolescent obesity management tools in-clinic.

2.2: I effectively provide healthy behavior lifestyle counseling for patients during well child visits.

2.3: I effectively provide healthy behavior lifestyle counseling for patients during child obesity visits.

2.4: Pediatric/adolescent patients follow my health behavior recommendations.

2.5: The current health care system provides sufficient resources for my pediatric/adolescent patients to make meaningful health behavior changes.

2.6: The current health care system provides sufficient continuity for my pediatric/adolescent patients to make meaningful health behavior changes.

2.7: I have sufficient training to provide healthy behavior lifestyle counseling for pediatric/adolescent patients.

2.8: I have sufficient training to provide obesity management for pediatric/adolescent patients.

2.9: Current EHR data supports health behavior lifestyle conversations with adolescents/children.

2.10: I see a need for tools like mobile health (mHealth) apps to help patients to develop healthy lifestyle habits.

(3) EHR weight management tool preferences

Choose all that apply

3.1: What health information would you like to see in an EHR BMI percentile over time tool to help address obesity in adolescent patients?  ❑ Blood Pressure percentile over time  ❑ Current weight in kilograms  ❑ Current weight in pounds (lb)  ❑ Change in weight since last clinic visit  ❑ Change in BMI percentile since last clinic visit  ❑ Systolic and diastolic blood pressure  ❑ Blood Pressure percentile  ❑ Most recent HbA1C  ❑ Most recent lipid panel: cholesterol, LDL, HDL, triglycerides  ❑ Most recent glucose Most recent liver function tests  ❑ Growth Charts  ❑ Other (specify below)

3.1.a: Other health information: _________

3.2: Which do you prefer when visualizing lifestyle data (i.e., fruits and vegetables or water intake) logged by your pediatric/adolescent patient?  o Line graphs  o Bar graphs

3.3: Which do you prefer when visualizing lifestyle data (i.e., fruits and vegetables or water intake) logged by your pediatric/adolescent patient?  ○ Monthly Averages  ○ Weekly Average

3.4: Which do you prefer in a graph?  ○ Combined weight and BP (line graph)  ○ Separate weight (or other biometric) in line graph

3.5: How would you want patient logged lifestyle data to flow into your clinic note?  ❑ As an autotext with an average over the past 4 weeks  ❑ An autotext with an average over the past 4 months  ❑ As an autotext with an average over the entire time period they have been logging the goal  ❑ Average with the minimum and maximum range  ❑ An option to copy and paste averages from the EHR visualization  ❑ Patient self-reported health behavior goals  ❑ Minimum nutrition and physical activity documentation requirements for well child visits  ❑ Other (specify below)

3.5.a: Other: _________

Not included are questions regarding demographics and CommitFit EHR tool evaluation.

Results

Focus Groups

Four family medicine, seven pediatric, and one “other” (Health and Clinical Psychology) faculty members in total participated in three focus groups. Three clinicians participated in two focus groups, and four clinicians participated in all three focus groups. Five clinicians participated in only one of the focus groups. There were six participants in the first focus group, six in the second, and seven in the third. Of focus group participants five (42%) identified as cis-male and seven (58%) identified as cis-female, two (17%) were 31 to 40 years, six (50%) were 41 to 50 years, and four (33%) were 51 to 60 years. Regarding practice length, three (25%) had been in practice for 5 to 10 years, while nine (75%) practiced for >10 years. Other health care providers (N.P., P.A.) were invited to participate in the focus groups, but none chose to participate. Participants in all three focus groups seemed engaged and enthusiastic about the need and development of a pediatric weight management EHR tool. In the qualitative analyses, we identified five major themes included below. After three focus groups, saturation was reached with no new themes identified. All participants engaged fully in the focus group with no one member dominating any of the focus groups.

Electronic Health Record Weight Management Tools Should Improve Clinical Efficiency

Clinicians expressed frustration with EHR tools or displays that did not provide patient health information when and where it was needed. They expressed a desire for tools that helped them be more efficient during clinic visits. Focus group clinicians identified several ways that EHR tools can help improve the efficiency of these visits including displaying relevant data in a way and at a time that is conducive to their workflow.

“I agree it needs to be something that's continually present whenever you open their chart so that you're just not relying on you know memory. You're looking back at old notes. It's something that's quickly and easily seen and accessible.” [FG 3]

“I think as long as it comes up in real time, like when you're accessing their chart. I think that would be the most helpful because that's day of it's when you need to know.” [FG 3]

Electronic Health Record Weight Management Tools Should Support Patient-Centered Communication

Clinicians were eager to partner with families, especially regarding lifestyle goal setting. They wanted EHR tools, such as graphs of the patient's self-reported health behaviors, that they could show to families to help visualize their progress, trends in weight, or health metrics. They expressed a desire to empower their patients to set health behavior goals for themselves and brainstorm ideas to help them achieve and maintain those goals.

“I think this is something that would be really helpful in both building common ground with the patient and potentially the parents. Wow really great job. I look as you've been increasing your fruits and vegetables; your blood pressure is going down. Isn't that great? I think that that helps lead into that conversation around like so what do you think? Do you think you wanna stick with this goal? Or do you think you wanna tweak it a little bit like whatever, so that's your decision making.” [FG 3]

“Are they working on it or what goal did we set? Like those kind of decision support tools? Yeah, it's great to have the growth chart to basically figure all that stuff out, but it would be super awesome to have a summary page that basically helps me see.” [FG 1]

Electronic Health Record Weight Management Tools should Improve Patient Continuity between Visits

One of the major barriers that clinicians identified was the time between visits. They wanted a tool that would bridge this gap without extra time or effort. Clinicians were enthusiastic about EHR tools including mHealth apps or patient-generated data that could be used to follow-up with patients between clinic visits to increase the likelihood of adherence to health behavior goals.

“I think that would be great because, alright, I'll write it in the notes you know, and then if the children want and the parents want us to write it down on a piece of paper we do that. I wish we had the time to go back and contact them, but I'm just being realistic to we don't, but if there was something easy to where you could just send a reminder.” [FG 1]

Electronic Health Record Weight Management Tools Should Reduce Documentation Burdens

Time and effort required for documentation was another major barrier. Clinicians favored tools that could easily transfer data into their notes to reduce documentation burdens. Although clinicians disagreed about the amount or type of patient data they would want to include in their notes, they all favored an easy way to transfer these data.

“I go through, and I find data that I want, I can just copy that to the clipboard and then I can say, you know, paste it into my note and then here's what I've reviewed from the… Health app, right? If the goal is to say I reviewed this with the patient and, if there were any changes in goals that were made, it sure would be nice that if it automatically gets posted to the clipboard and I just paste it in.” [FG2]

Clinicians Trust Patient Data Entered in Real-Time Over Recalled Data

The final focus group theme was regarding the comparison of clinician trust in data entered in real time by the patient through a portal or app, compared to data recalled by the patient during the clinic visit. Focus group clinicians felt that real-time patient-entered data would be more accurate since it was less likely to be affected by recall bias.

“It's like when you ask: “When did you quit smoking?” “An hour ago”, right? I mean, so I mean this this really gives you much richer data. It's honest and it has all the motivational factors built into it, so I would be more inclined to follow this. Then, sort of that retrospective, I think you can do a pretty good 24-hour diet recall after that it sort of like starts to get to be fiction.” [FG 2]

Questionnaire

The rate for completed questionnaires in total at all institutions was 76% (n = 52) fully completed and 23% (n = 16) partially completed questionnaires, which were excluded from analysis. The participation rate at MU was 28% (41 responses of 148 total faculty emailed). Other institutions, all in Missouri (St Louis University, Children's Mercy in Kansas City, and St. Francis, in Cape Girardeau), had 15 respondents out of an estimated 113 clinicians, 13% response rate. The NAPCRG researcher forum had three respondents, for 27 total responses from outside institutions, with 67% (n = 18) completed and 33% (n = 9) partially completed questionnaires. Fifty-two completed surveys were included in the analysis. Most of the respondents were physicians (92% doctors of medicine, 4% doctor of osteopathic medicine), but 4% were other health care providers (one doctor of nursing practice, one master of science in nursing). The most common specialty was family medicine (54%), followed by pediatrics (44%), and other (2%).

Most respondents felt that they provided effective lifestyle counseling during obesity visits (64, mean on a scale of 1–100 for all scaled questions) and during well-child visits (60) and that they received sufficient training to provide obesity management (53). However, fewer felt that they had effective child obesity management tools in clinic (41) or that the health care system provides sufficient resources (28) or continuity for follow-up (37). Providers felt that current EHR data did a poor job supporting health behavior lifestyle conversations (42) and that there was a need for tools like mobile health (mHealth) apps to help facilitate patient behavior change (70) ([Fig. 2]).

Fig. 2 Questionnaire results of Clinician Current Needs and Barriers for Pediatric and Adolescent Weight Management Care in the clinics.

Of the completed surveys for the question regarding what health information clinicians would most like to see in a pediatric weight management tool, the most popular responses were change in weight since the last clinic visit (96%), growth charts (90%), BMI percentile over time (85%), and current weight in pounds (85%). These were preferred over current weight in kilograms (28%). Of the lab options, the most recent lipid panel (77%) and hemoglobin A1c (73%) were preferred over the most recent liver function test (56%) and most recent glucose (43%) ([Fig. 3]).

Fig. 3 Questionnaire Results Health Information Clinicians want in a Pediatric Weight Management EHR tool.

Discussion

We conducted this mixed-methods study which included clinician focus groups and questionnaires to develop a more thorough understanding of current information needs to support clinicians treating pediatric obesity. We discovered that existing EHR weight management tools fall short of meeting the needs of clinicians. However, by incorporating clinician input, we can develop tools that address barriers and enhance the efficiency and effectiveness of obesity clinical encounters. Most participants that felt the current health care system and EHR tools do not adequately support pediatric weight management ([Fig. 2]). This was supported in the physician focus groups with a consensus that the current health care systems and EHRs lack effective weight management data visualization and tools.

Focus group clinicians identified several barriers in the current system that reduce the effectiveness of pediatric weight management visits that were presented as focus group themes. EHR tools that work to try to reduce these barriers will be more likely to be adopted by clinicians and have a greater impact on patient care.[20] [21] [22] [23] Major themes identified by focus group clinicians of ways that EHR weight management tools can overcome these barriers were that EHR tools should improve clinical efficiency, support patient-centered communications, improve patient continuity between visits, and reduce documentation burdens. These themes are consistent with previous studies which indicate that EHR and digital health tool adoption is facilitated by improved productivity, better quality of care, strong usability[24]as well as true clinical value.[25] In the literature, clinicians also prefer a tailored approach for obesity management, in addition to expert guidelines.[16]

Increased workload, expectations, and documentation burdens with patient portals and the EHR, all increase the burden for clinicians to do more, in shorter periods of time, contributing to physician burnout.[26] [27] Well-child visits, where the majority of health behavior counseling for children occurs, are very busy, with updated medical history, anticipatory guidance, vaccinations, and medication management. Pediatric weight management clinic visits can also be very busy, with review of BMI percentile, nutrition and physical activity histories and counseling, reviewing labs and medication, or lifestyle management, also often occurring in 20 minutes or less. Clinicians preferred EHR tools that could increase the efficiency of these visits.

Clinicians in this study preferred EHR tools that displayed a change in weight since last visit, growth curves, current weight in pounds (not kilograms), and BMI percentile over time ([Fig. 3]). Although most of the EHRs display data clinicians reported wanting, such as BMI charts, few EHRs display the data in a consolidated weight management tool and with patient logged health behavior data. These deficits likely contributed to the questionnaire's poor response regarding effective child obesity management tools in the clinic (item 41) and current EHR data support health behavior lifestyle conversations (item 42).

Clinicians preferred EHR tools that could help facilitate patient-centered communication, such as graphs of the patient's weight, blood pressure, and self-reported health behaviors ([Fig. 1]). Graphs that display weight or BMI percentile over time (which was preferred by 85% of survey respondents) can provide helpful feedback of how the patient's health behaviors or life events influence their weight or other health metrics.[28] Clinicians can present these graphs to patients during clinic office visits and use patient-centered communication to engage with them to help develop a weight loss plan based on their previous successes and failures. The clinic is likely the only source for patients to see their BMI percentile or weight graphed over long periods of time, and these data can be very meaningful.

Patient-centered communication and counseling can be further enhanced by the developed CommitFit EHR data visualization tool because the clinician can review the patient-logged health behavior data (fruits and vegetable consumption, etc.) with the patient in the clinic and help them understand the relationships between their health behaviors and health outcomes such as weight and blood pressure.

Clinicians also looked to EHR tools to help with the continuity of patient care between clinic visits. This was consistent with the survey response that the health care system does not provide sufficient continuity (mean of 36/100). Increased frequency of clinic visits, with patient visits scheduled monthly, has been identified as a more effective approach for pediatric weight management[29]; yet, shortages of providers and increased patient panel sizes make this challenging.[30] [31] Emerging technologies offer opportunities, if developed and monitored robustly, to extend the continuity of clinicians between visits. However, new technology should not increase clinician burdens, such as causing increased patient messaging between visits.

Time and stress associated with increased documentation requirements are a major contributor to physician burnout.[32] [33] [34] EHR tools should strive to reduce documentation burdens by providing clinical note autopopulating templates or the ability to quickly and easily paste or otherwise transfer data into their notes. Not only would this increase documentation efficiency, but it reduces the risk of transcription errors when clinicians find information in one area of the EHR and then manually type or dictate it in the clinic note. However, computerized systems can also increase the risk of medical error if not reviewed closely by the clinician.[35] Previous studies have shown that integrating patient-generated health data into clinical care can be effective in managing chronic conditions[36] and improving patient-clinician engagement and communication between clinic visits[37] [38] [39] [40] [41]

The final focus group theme was that clinicians trusted data entered in real time by the patient was more than patient-recalled data. This offers a significant opportunity for the additional development of EHR tools that (1) collect health data from the patient before clinic visits, (2) integrate these data into the EHR in a useful way, and (3) are available during the clinic visit for use by the clinician and patient. The popularity of wearable health tracking devices, such as Apple Watch and Fitbit, creates the availability of vast amounts of patient-tracked health data. Technology can be developed to assist in sorting useful health data from noise and false positives, and effectively delivering these data to clinicians in a timely, secure, and useful fashion. EHR tools should enable clinicians to respond to these data in a way that does not increase previously discussed burdens and for which they are compensated.

Limitations

There were several limitations to this study. To increase the sample size and generalizability of the questionnaire, it was shared with pediatric colleagues at other clinics and academic institutions in Missouri and on the NAPCRG research forum. We were unable to determine the number of clinicians this was shared with so we cannot determine the response rate at these outside institutions, although we estimate it to be about 13%. Because the focus groups were conducted toward the end of coronavirus disease when restrictions were still in place, they were conducted virtually. While clinicians were largely familiar with each other and seemed to participate freely, it can be difficult to interpret body language and participation over virtual platforms. There was also a relatively small qualitative sample (12 clinicians); however, a saturation of themes was reached with limited utility of conducting additional focus groups. In the future, we will consider scheduling interviews with clinicians who were unable to attend the focus groups. Finally, all focus group participants and most of the survey respondents practiced medicine in Missouri. However, the themes and trends discussed are likely applicable to other regions of the United States and beyond. Additional studies with larger sample sizes and at other regions with different EHR tools would be useful to further understand barriers to effective weight management and to help develop EHR tools to overcome those barriers.

Conclusion

Questionnaire and focus group data from our mixed method study suggest that the health care system status quo and currently available EHR tools do not sufficiently support clinicians working to manage pediatric obesity. Although clinicians report sufficient training to provide obesity management and behavior lifestyle counseling, they report that the health care system does not provide sufficient resources or continuity and that the current EHR data displays do not support healthy behavior conversations. EHR weight management tools can help overcome these barriers if they improve clinical efficiency, support patient-centered communication, improve patient continuity between visits, and reduce documentation burdens. Clinicians trust data entered by patients in real time more than recalled data shared in the clinic. EHR weight management tools will be more likely to overcome these barriers if the tools are developed and tested with clinician input. The enthusiasm of clinicians toward these EHR tools suggests a strong potential to improve patient health behaviors and ultimately reduce pediatric obesity.

Clinical Relevance Statement

Currently, available EHR tools do not sufficiently support clinicians working to manage pediatric obesity. EHR weight management tools offer significant opportunities to overcome current barriers if they improve clinical efficiency, support patient-centered communication, improve patient continuity between visits, and reduce documentation burdens. EHR tools should be developed and tested with clinician input.

Multiple Choice Questions

1. Which of the following did clinicians who responded to the study questionnaire identify as the most useful clinic data to be included in an EHR weight management tool?

   • Change in weight since the last clinic visit

   • Most recent glucose

   • Most recent liver function tests

   • Weight in kilograms

   Correct Answer: The correct answer is option a, change in weight since last clinic visit (96%). This was followed in the study by growth charts (90%) and BMI percentile over time (85%). The other answers were less useful: C, most recent liver function test (56%), B, most recent glucose (43%), and D, weight in kilograms (28%).

2. Which of the following did focus group clinicians identify as important for an EHR weight management tool?

   • EHR weight management tools should be universally available to all clinicians

   • EHR weight management tools should improve patient continuity between visits

   • EHR weight management tools should only display data entered in the clinic

   • EHR weight management tools should increase billable services

   Correct Answer: The correct answer is option b, EHR weight management tools should improve patient continuity between visits. Focus group clinicians trusted patient-entered real-time data over data obtained in clinic (C) and recommended that it be included in the EHR weight management tool. A and D were not discussed by focus group clinicians or included in this paper.

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