Feasibility,Implementation and Outcomes of Tablet-Based Two-Step Screening for Adult ADHD in Primary Care Practice

Abstract

Background: Primary care clinicians need to recognize and diagnose Adult ADHD (AADHD). We tested the feasibility and outcomes of a two-step screening process for AADHD in primary care. Methods: Seven practices screened patients using computerized surveys. Patients screening positive completed the AADHD Quality of Life (AAQoL). We explored the impact of screening on workflow and the acceptability to patients, and identified key barriers/opportunities to continuing screening. Results: Of the 711 participating adults, 188 (26.4%) screened positive, of which 32 (17.0%) had scores at least one standard deviation below means on two or more domains on the AAQoL (average 23.6 ± 7.3). These 32 individuals represented 4.5% of all participants. Clinicians were willing to screen, diagnose, and treat AADHD, but need additional resources. The screening process and technology was acceptable to patients and staff. Conclusions: A two-step screening method shows promise for routine screening for AADHD.

Keywords

attention deficit hyperactivity disorder primary care screening

Introduction

ADHD is one of the most common childhood disorders and can continue through adolescence and into adulthood. Current research indicates that the condition has unique diagnostic features in adulthood and does not necessarily represent the same condition as expressed in children. The epidemiology of ADHD over the past 15 to 30 years has established it as affecting about 4% to 5% of the adult population (Kessler, Demler, et al., 2005). Adverse outcomes of ADHD related to overall health, longevity, quality of life (QoL), and safety have been well documented (Nigg, 2013). Persons with ADHD, in particular in adulthood, have a twofold risk of premature death, usually from unnatural causes such as accidents or suicide (Barbaresi et al., 2013; Dalsgaard, Ostergaard, Leckman, Mortensen, & Pedersen, 2015). Adults with ADHD are more likely than those without to have an additional comorbid psychiatric disorder (Barbaresi et al., 2013). Emerging evidence points to substantial somatic disease comorbidity such as asthma, metabolic disorders, migraine, and other chronic conditions with ADHD in adults (Instanes, Klungsoyr, Halmoy, Fasmer, & Haavik, 2018). Previous studies (Harpin, 2005) also reported an association between ADHD and arrests and incarcerations (Knecht, de Alvaro, Martinez-Raga, & Balanza-Martinez, 2015; Mannuzza, Klein, & Moulton, 2008), unsafe driving (Fuermaier et al., 2017) and other risky behaviors (Breyer et al., 2009; Sarver, McCart, Sheidow, & Letourneau, 2014), unemployment (Halmoy, Fasmer, Gillberg, & Haavik, 2009), and difficulties in the workplace and social interactions (Adamou et al., 2013).

Recent evidence shows that ADHD is underrecognized and undertreated in adults (Ginsberg, Quintero, Anand, Casillas, & Upadhyaya, 2014). Studies in children found that screening for ADHD may be beneficial for identifying children who might benefit most from a detailed ADHD assessment and access to effective interventions and treatments (Sayal, Letch, & Abd, 2008). There is paucity of studies on ADHD in adults, and to our knowledge, few screening studies have been conducted. Several studies highlight clinical challenges in assessment, diagnosis, and management of adult ADHD (AADHD; Culpepper & Mattingly, 2010), including issues related to provider discomfort with prescribing stimulants (Kovshoff et al., 2013). Moreover, in the United States currently, there are no evidence-based guidelines to assist primary care providers with assessment, treatment, or referral for their adult patients with ADHD symptoms. According to the universal principles and practices of disease detection (Wilson & Jungner, 1968), one of the key questions that needs to be addressed is whether it is feasible to carry out the relevant screening, diagnostic, and timely intervention practices in a population-based fashion with existing resources. Before we can even consider implementing screening for AADHD, we must address the adoption and implementation of the screening practices using available screening tools that can be implemented in an office workflow and that reliably identify patients for which additional diagnostic workup is warranted.

The objective of this article is to present the result of the AADHD Screening pilot project. The central aim of the project was to test the feasibility and effectiveness of using an opportunistic tablet computer-based two-step screening approach that included a brief symptom checklist and an assessment of QoL. In addition, we assessed the prevalence of positive ADHD screening results among adult primary care patients and association of a positive screen with decreased QoL.

Method

Study Overview

We assessed provider knowledge and attitudes about AADHD screening via survey, then evaluated the implementation of a screening method to detect ADHD in adults in seven primary care practices. We gathered qualitative and quantitative data regarding the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) of the screening method (Glasgow, Vogt, & Boles, 1999). The [IDENTITY MASKED–1] conducted the study with approval by the [IDENTITY MASKED–2] institutional review board (IRB).

Provider Survey

The survey (available upon request) included questions about provider knowledge of and willingness to screen, evaluate, and treat ADHD in adults, and a question of practice interest in the screening implementation study. The web-based survey was offered to [IDENTITY MASKED–1] members.

Tablet System

Talking Survey™ LLC system was used in conjunction with Asus TF300T Android Tablet computers. The Talking Survey system was customized to deliver several surveys, survey scores, and the outcomes reports for every patient. The reports were instantaneously generated, were viewable by clinicians/office staff on the tablet, and were uploaded in the host server. Each participating practice received a unique user ID and a password to access the reports. Portable tablets were connected to the clinic’s secure wireless network; tablets sent data over an encrypted network connection. All patient-level data were de-identified prior to transfer to the research team.

Practice Inclusion Criteria and Study Expectations

The practices were invited to participate in the screening implementation if they indicated interest; were willing to implement screening for at least 6 and up to 16 weeks; were willing to identify, recruit, and screen a minimum of 150 patients; had wi-fi connection; and were willing to be interviewed. A purposeful sampling method was used to include participating practices for an equal distribution of private practices, residency practices, and federally qualified health centers. Practices received one-on-one training in the use of the tablets.

Patient Eligibility and Recruitment

Adult patients (aged 18-44) scheduled for a visit with participating practices were invited by the site coordinator (practice staff) to complete a one-time self-administered screening survey for ADHD on a tablet. A sequential study identification number was generated for each patient. The patients were presented with a project summary on the tablet and instructions to ensure they could use the tablet touchscreen. All participants provided informed consent.

Screening Tools and Administration

We implemented opportunistic tablet-based two-step screening that included a brief symptom checklist and an assessment of QoL. The AADHD Self-Report Scale (ASRS-v.1) Symptom Checklist (“http://www.hcp.med.harvard.edu/ncs/ftpdir/adhd/6Q_ASRS_English.pdf,”; Kessler, Adler, et al., 2005) and the AADHD Quality of Life Questionnaire (AAQoL; used with permission) (Brod, Johnston, Able, & Swindle, 2006) were used in tandem. ASRS-v.1 has a sensitivity of 68.7%, and specificity 99.5% (Kessler et al., 2007). The screening was planned to take place in the waiting room before the scheduled visit.

Patients first completed the ASRS-v1.1 Symptom Checklist. If the outcome indicated that the individual had symptoms highly consistent with AADHD, the tablet presented the AAQoL. The AAQoL survey includes four self-assessed domains: life productivity, physiological health, life outlook, and relationships, plus an overall score. A patient-specific report was generated and available to the providers. The subsequent treatment or referral was not a part of this pilot project.

Data Collection

The research team received the following de-identified patient data: age and gender of patients who agreed to complete the survey, including those who started but did not finish, and all survey responses.

Statistical Analyses

After the data were cleaned for duplicates and survey cancelations, descriptive statistics (overall, different age and gender groups, and life domains) of the respondents were computed. Prior to the analyses, to determine the patients who may have lower QoL possibly due to AADHD, a threshold of one standard deviation below the mean of all patients was set for those patients who screened positive on ASRS. Given that we did not evaluate the participants for the actual presence or absence of AADHD, we assigned those who were screened positive for AADHD and had lower scores on two or more QoL domains to a subgroup that we considered as likely to be affected by ADHD. We used this approach as a reasonable proxy for prevalence, based on the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) requirement for the AADHD diagnosis to have two life domains affected by the symptoms and to reduce the likelihood of false positive results.

The data were checked for assumptions, and non-parametric tests were used due to non-normal data distribution. Missing data were excluded on a case-by-case basis. For the comparison of gender among age groups, χ² test was used. We used the Mann–Whitney U test for the comparison of the patients who triggered the AAQoL to patients with AADHD and the comparison of male patients and female patients with AADHD. For the comparison of rural, suburban, and urban patients, we used the Kruskal–Wallis H test because the assumption of normality did not hold and the already small sample size was split across three groups. An α of 0.05 was used throughout the analysis. All analyses were conducted with SPSS.

Results

REACH and Effectiveness—Patient Level Outcomes of Screening

A total of 780 patients agreed to participate, of which 61 canceled before finishing (7.8%) and 8 were duplicate entries (1.0%). Reasons for canceling before finishing included the following: experienced technical issues, pressed wrong button (52.5%); called for appointment (24.6%); no longer wish to participate (14.8%); felt uncomfortable with questions (4.9%); and distracted/unable to focus (3.3%).

The final sample included 711 patients (91.2% of original sample). Table 1 displays a descriptive profile of the participants. Out of the original 188 respondents who scored positive on the ASRS-v.1 Symptom Checklist, six did not complete the AAQoL survey resulting in 182 patients included in the analyses of QoL (Table 2). There was no difference in age or gender distribution between those who screened positive and those who screened negative.

Table 1.

Adult ADHD Self-Report Scale (v.1) Symptom Checklist Results by Age/Gender.

	Total screened (N = 711; n (%))	Screened negative(n = 523; n (%))	Screened positive (n = 188; n (%))	p value
Female 18-29	197 (27.7)	149 (32.4)	48 (10.4)
Female 30-44	263 (37.0)	195 (42.4)	68 (14.8)
FEMALE subtotal	460 (64.7)	344 (74.8)	116 (25.2)
Male 18-29	94 (13.2)	61 (24.3)	33 (13.1)
Male 30-44	157 (22.1)	118 (47.0)	39 (15.5)
MALE subtotal	251 (35.3)	179 (71.3)	72 (28.7)
By gender; 18-29				.06
By gender; 30-44				.82
By gender; all				.32

Table 2.

Assessment of Quality of Life in Those Who Screened Positive on ASRS-v.1.

	Total screened positive (n = 182)	ASRS-v.1 positive with 0 to 1 QoL domains affected (n = 150)	ASRS-v.1 positive with 2 or more QoL domains affected (n = 32)	Test statistics
	Total screened positive (n = 182)			Mann–Whitney U	p value
Overall	45.3 ± 14.6	49.9 ± 11.2	23.6 ± 7.3	91.0	.00
Life Outlook	51.4 ± 15.5	54.6 ± 13.9	36.8 ± 14.1	766.0	.00
Life Productivity	44.6 ± 18.0	49.4 ± 15.1	21.7 ± 12.0	357.5	.00
Psychological Health	35.8 ± 19.5	41.2 ± 16.9	10.6 ± 7.6	143.5	.00
Relationships	49.8 ± 18.7	55.1 ± 14.4	25.0 ± 16.7	436.5	.00

Note. All scores are presented as M ± SD. ASRS = Adult ADHD Self-Report Scale; QoL = quality of life.

The overall score on the AAQoL for those who screened positive on ASRS was 45.3 ± 14.6. Of the 182 patients who screened positive on the ASRS-v.1 and completed the AAQoL, 30 patients (16.5%) had one life domain significantly below the group mean; 120 (65.9%) patients did not score significantly below the mean for any life domain; and 32 (17.6%) individuals had scores on two or more domains of life at least one standard deviation below the group mean (Table 3). These 32 individuals represented 4.5% of all participants in the study.

Table 3.

AADHD Quality of Life Results.

Number of domains with a score of one or more standard deviation below the mean	Frequency	Percent
No Quality of Life Domains Affected	120	65.9
1. Domain Affected	30	16.5
2. Domains Affected	16	8.8
3. Domains Affected	8	4.4
4. Domains Affected	8	4.4
Total	182	100.0

Next, we compared the scores on QoL measures including the overall with individual domain scores of (a) individuals who screened positive on ASRS and had zero or one domain of QoL affected (n = 150) with (b) those who screened positive and had two or more domains of QoL affected (n = 32; see Table 2). The individuals who screened positive with ASRS and had two or more domains of QoL affected had significantly lower scores for QoL overall and across all four life domains.

The QoL did not differ by gender for the 32 individuals who had symptoms consistent with AADHD in the overall scores and the scores for Life Outlook, Life Productivity, and Psychological Health (see Table 4). The male patients in this group, however, had significantly lower scores in the Relationships domain than the females (15.0 ± 12.2 vs. 28.3 ± 16.9, p < .05).

Table 4.

QoL by Gender and Setting in Participant Who Screened ASRS-v.1 and Have 2 or More QoL Domains Affected.

QoL domains	Male (n = 8)	Female (n = 24)	p value	Rural (n = 19)	Suburban (n = 5)	Urban (n = 8)	p value
Overall	22.8 ± 8.2	23.9 ± 7.1	.88	23.1 ± 6.2	21.2 ± 7.0	26.4 ± 9.7	.57
Life Outlook	37.0 ± 19.6	36.8 ± 12.3	.66	31.2 ± 7.7	36.4 ± 9.9	50.4 ± 19.3	.01
Life Productivity	23.0 ± 12.2	21.3 ± 12.2	.36	22.6 ± 10.6	17.3 ± 12.1	22.4 ± 15.7	.70
Psychological Health	12.5 ± 7.0	9.9 ± 7.8	.38	10.1 ± 7.5	10.8 ± 6.3	11.5 ± 9.4	.98
Relationships	15.0 ± 12.2	28.3 ± 16.9	.05*	28.4 ± 17.0	21.0 ± 19.2	19.4 ± 14.3	.29

Note. QoL = Quality of Life; ASRS = Adult ADHD Self-Report Scale.

When QoL of 32 individuals who had symptoms consistent with AADHD was compared with patients in rural, suburban, and urban settings, there was no difference in the Overall, Life Productivity, Psychological Health, and Relationship scores. However, the Life Outlook score was significantly different (p < .01) for patients in a rural setting having the lowest score (31.2 ± 7.7) and patients in an urban setting having the highest score (50.4 ± 19.3; see Table 4).

Adoption, Implementation, and Maintenance at the Practice Level

Adoption—Readiness to screen for AADHD in primary care

Ninety-seven physicians responded to the AADHD readiness to screen survey indicating their interest in the topic of AADHD. Related to the RE-AIM practice-level Reach metric, 38 respondents indicated interest in the pilot study (40% out of 97 respondents). Of those, seven practices were selected to participate in the screening study.

Implementation

All participating practices were able to implement the screening administration protocol, recruit patients, and complete evaluation assessments. Physicians and practice staff reported in the interviews that patient selection and survey administration varied by site (Table 5). For some sites, staff scheduled eligible patients for longer visits so that the surveys could be administered before they were seen for care. In others, staff provided the tablet to patients after they were roomed, but before they saw the clinician. Regardless of administration protocol, few patients declined to participate or failed to complete the surveys because of difficulty in using the tablet or because they found the survey to be unacceptable.

Table 5.

Physician and Support Staff Themes Related to Implementation.

Medical/health	Process
Physician themes
Important, underrecognized condition	Want report at the point of care
Concern over moving to diagnosis (and who should do that; e.g., them or psychiatrist)	Trust their staff to work out kinks
Valuable to know QoL	Need streamlined workflows to coordinate with office activities and other screening
Insurance issues may influence care	Need EHR-tailored ways to accommodate screenings
During preventive visits, could screen for additional health concerns (e.g., binge drinking, depression, sexual dysfunction, exercise); Difficult to determine what to screen for as all add time/cost	Could complete short screenings (6 questions) in waiting room or complete them at home prior to visit
No guidelines for Adult ADHD; Need evidence	Positive cases—either patients or providers suspected ADHD for some of them—screening helped to identify few new cases
Comfortable treating most cases in office, refer occasionally
Support Staff Themes
Weak, but concern and surprise of (+) cases	Time and efficiency to accomplish screening
Weak, mention of f/u issues for (+) cases	Concern/challenged with specific features of the tablet, upload site and data access
Could use technology to screen for other issues (if brief screeners)	Accepted need to tailor workflow to accomplish; support to do so (by practice/lead clinician) was implicit
Some patients had wondered if they had ADHD, and wanted to see results	Technology accepted by patients
As a Medical Assistant, now realizes Adult ADHD is important	Report in EMR every morning
Screening was a good idea—will help providers to ID patients who need treatments

Note. QoL = Quality of Life; ADHD = Attention deficit hyperactivity disorder.

Clinicians in the pilot study were quite concerned about the patients who scored positive because, although the clinicians admitted to suspecting behavioral health issues, they were surprised that patients’ behavioral health significantly compromised their QoL. Clinicians recognized the value of assessing QoL. They also expressed concerns related to barriers to post-screening diagnostic work-up and management including unclear roles in who should conduct diagnostic evaluation, absence of evidence-based treatment guidelines, and external barriers such as insurance coverage.

Maintenance

All participating practices reported at the 6-month follow-up that they did not maintain the screening process after the intervention period ended.

Discussion

We explored clinician perceptions, behaviors, and readiness to screen, diagnose and treat adult patients with ADHD in adults. Family medicine physicians and their staff are very interested in AADHD and are willing to use technology to screen and assess QoL of patients who may have the condition. The successful implementation of a two-step AADHD screening process indicates that patients find the screening process acceptable and that practice staff can accomplish the screening with little disturbance to practice workflow. The tablet-administered data collection did not disturb patients or practices. Other studies reported, use of technology is acceptable and more accurate for collecting sensitive patient information and preferable over an interpersonal interview or paper (Brod et al., 2006; Cook et al., 2007; Dupont et al., 2009). From the feasibility standpoint, the practices were able to recruit the target number of patients and complete the screening on the majority of enrolled patients. Given that all of the participating patients visited the clinic for reasons other than to complete the ADHD screening, the screening implementation during routine practice visits seems feasible as designed.

Overall, 26.4% of the population scored positive on the ASRS Symptom Checklist. This rate of positive screening results is slightly higher than what has been reported in the literature and needs to be further explained (Adler, Guida, Irons, Rotrosen, & O’Donnell, 2009; Hines, King, & Curry, 2012; Kessler, Adler, et al., 2005). Given that we did not confirm the diagnosis of ADHD or other mental or behavioral health problems in study participants, it is possible that the prevalence of ADHD-like symptoms is higher in primary care than previously reported. Alternatively, the relatively high positive screen rate may be due to low sensitivity of the ASRS screening instrument (reported sensitivity ranges between 61.0% and 68.7%; Dakwar et al., 2012; Kessler, Adler, et al., 2005), when the symptoms could be attributed to another behavioral health issue. The fact that 120 patients did not score significantly below the mean for any life domain on the AAQoL likely indicates that they either are coping well with symptoms even if caused by undiagnosed/untreated AADHD or their symptom may due to current levels of psychological distress. When compared with previously reported scores from persons diagnosed with ADHD or identified via screening in other studies, the overall AAQoL and QoL domains scores of participants who screened positive in our study are comparable (Brod et al., 2006). Our study results contribute to the evidence that AADHD negatively affects QoL. The patients who had symptoms consistent with AADHD, defined as those patients who scored positive on the six-question ASRS-v.1 who could meet the clinical criteria of having AADHD by having at least two QoL domains significantly impaired, had significantly lower life domain scores across all four QoL domains. Unfortunately, the cut off scores for the AAQoL and normative data are not well established to provide more accurate estimates. More research that includes patient follow-up with more specific behavioral health assessments should be undertaken to further explore the effects of ADHD on QoL in adults.

A two-step screening method for AADHD that included QoL assessment is potentially useful in primary care. The addition of QoL assessment concentrated the group from 26.4% screened positive to 4.5% who had symptoms consistent with AADHD and lower AAQoL scores. This prevalence is comparable with the reported prevalence of ADHD in the adult population (Kessler et al., 2006; Simon, Czobor, Balint, Meszaros, & Bitter, 2009). Findings from this pilot study affirm that AADHD is probably underdiagnosed among 18- to 44-year-old patients seen in family medicine clinics.

Existing evidence points out that ADHD is more prevalent in males, although other studies have reported gender differences for the standard 18-item ASRS, with females reporting a higher frequency of symptoms (Gray, Woltering, Mawjee, & Tannock, 2014). However, in our study, the relative prevalence of positive screening scores and the proportion of patients who had symptoms consistent with AADHD was similar between men and women. Furthermore, the male patients who had a positive screening score and low QoL scored slightly lower in the Relationships domain than the female AADHD patients. This finding contrasts with the results from studies that show that some aspects of relationships such as intimacy and self-esteem are more affected by ADHD in women than among men (Ben-Naim, Marom, Krashin, Gifter, & Arad, 2017; Quinn & Madhoo, 2014). The results of our study confirm that the life domains are impaired in men and women with symptoms consistent with AADHD for all four domains; further research is needed to explore what aspects of relationships are more difficult for male than female patients.

The QoL of those patients who had symptoms consistent with AADHD was comparable with practice location in urban, suburban, or rural areas, except for the Life Outlook score that was lower in the patients from rural practices when compared with suburban and urban practices. This observation is in line with other studies in mental health that indicated negative life outlook in rural areas (Barry, Doherty, Hope, Sixsmith, & Kelleher, 2000; Hirsch, 2006).

As stated above, we only assessed the feasibility, implementation, and early effectiveness of the opportunistic screening process in practice to identify individuals with symptoms consistent with AADHD. Further research is needed to address the implementation and effectiveness to determine which screening type (population/mass, opportunistic, or targeted) is most cost-effective and to establish optimal screening frequency in adults.

One of the limitations of our study is that we have not assessed whether individuals who screened positive had a diagnosis of AADHD or were assessed for it. In addition, the estimates for symptom prevalence that would be consistent with AADHD is based on a combination of positive screening and two areas of life affected by the symptoms, which is consistent with the definition of ADHD per DSM-5, but may not be a true representation of the prevalence of ADHD diagnosis. We have not tested the effectiveness of the screening for AADHD on patient-related outcomes or the clinical decision-making process. Similar to other studies in AADHD, we excluded older individuals, so the symptom prevalence and their association with QoL of older persons who may have symptoms consistent with AADHD are not clear. Future studies should also explore the effectiveness of the screening on the important outcomes in a patient-centered way and expand the age range to include adults older than 44 years. It will be important to explore the required support, facilitators, and barriers related to screening and post-screening follow-up in primary care and to assess to what extent and under which conditions the screening is sustainable. Finally, results of our study need to be interpreted with caution as they may not be generalizable to all practice types and contexts. Due to the pilot nature of this study, a limited number of practices who self-selected into the study were included.

Conclusion

AADHD is a prevalent condition among adults and is challenging in primary care. Multiple barriers to optimal patient care exists, of which many are associated with insufficient provider knowledge and comfort level with assessment, diagnosis, and management of AADHD. Tablet-based two-step screening that includes a brief symptom checklist and an assessment of QoL is found to be feasible and effective in identifying symptoms that could be indicative of AADHD or potentially other similar behavioral issues that negatively affect QoL.

Footnotes

Acknowledgements

The authors thank all participants of this project. We would like to acknowledge the American Academy of Family Physicians (AAFP) National Research Network for providing essential expertise, staff, and support. Parts of the early findings reported in this article were presented at the Annual Meeting of the North American Primary Care Research Group (NAPCRG), November 2013. Dr. Natalia Loskutova had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Masks for identifying labels in the manuscript file:

1: American Academy of Family Physicians National Research Network (AAFP NRN)

2: American Academy of Family Physicians

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by the American Academy of Family Physicians (AAFP) Foundation and made possible through funding by Shire US Inc. The opinions expressed in this work are those of the authors and do not necessarily represent those of Shire US Inc. The sponsor had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

ORCID iD

Natalia Y. Loskutova

Author Biographies

Natalia Y. Loskutova, MD, PhD, is currently a senior investigator and director of Evaluation at the American Academy of Family Physicians, National Research Network where she focuses on practice-based patient centered research and quality improvement initiatives applicable to primary care. Her primary research expertise is practice-based pragmatic study designs, implementation research, data collection methodology and qualitative and quantitative evaluation. Her research studies focus on aging and cognitive health, mental and behavioral health, and improved preventive services, care delivery and patient safety in primary care.

Elisabeth Callen, PhD, GStat, is a senior data scientist with the American Academy of Family Physicians National Research Network. She focuses on providing data management and analysis to a variety of practice-based patient-centered research studies.

Richard G. Pinckney, MD, MPH is an associate professor of Medicine, Primary Care Internal Medicine at the University of Vermont. His interests are in primary care-based interventions, provider education and research projects on the topic of mental and behavioral health.

Elizabeth W. Staton, MSTC, is a senior instructor in the Department of Family Medicine, University of Colorado School of Medicine. She collaborates frequently with the staff of the American Academy of Family Physicians National Research Network. She earned her bachelor’s degree in English and biology from Regis University and her master of science in technical communication from the University of Colorado Denver. She works closely with physicians and researchers to develop research concepts into written protocols, grant applications, and papers.

Wilson D. Pace, MD, is a professor Emeritus of Family Medicine, past Green-Edelman Chair Emeritus for Practice-based Research at the University of Colorado Denver, and past director of the American Academy of Family Physicians National Research Network. His research focuses on patient centered health information technology, behavioral change, practice reorganization and patient safety.

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