OBJECTIVES:

Established guidelines from the American Academy of Pediatrics for the care of patients with Down syndrome are often not followed. Our goal was to integrate aspects of the guidelines into the electronic health record (EHR) to improve guideline adherence throughout a child’s life span.

METHODS:

Two methods of EHR integration with age-based logic were created and implemented in June 2016: (1) a best-practice advisory that prompts an order for referral to genetics; and (2) a health maintenance record that tracks completion of complete blood cell count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study. Retrospective chart review of patients with Down syndrome and visits to locations with EHR integration (NICUs, primary care centers, and genetics clinics) assessed adherence to the components of EHR integration; the impact was analyzed through statistical process control charts.

RESULTS:

From July 2015 to October 2017, 235 patients with Down syndrome (ages 0 to 32 years) had 466 visits to the EHR integration locations. Baseline adherence for individual components ranged from 51% (sleep study and hemoglobin testing) to 94% (echocardiogram). EHR integration was associated with a shift in adherence to all select recommendations from 61.6% to 77.3% (P < .001) including: genetic counseling, complete blood cell count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study.

CONCLUSIONS:

Integrating specific aspects of Down syndrome care into the EHR can improve adherence to guideline recommendations that span the life of a child. Future quality improvement should be focused on older children and adults with Down syndrome.

Down syndrome has an incidence of ∼1 in 800 births, making it the most common live-born trisomy and chromosomal condition.1 Health supervision recommendations from the American Academy of Pediatrics (AAP) are used to guide care and include chromosome analysis and genetic consultation.2 Care recommendations continue throughout the life span and include both periodic and 1-time examinations and/or tests.2 Unfortunately, the AAP guideline components are often not followed.3,9 Several types of interventions have been shown to improve adherence.6,8,9 Surveyed pediatricians, who comprise a key stakeholder group, suggested integrating components directly into the electronic health records (EHRs) to improve adherence.6 

The integration of guideline recommendations into the EHR provides a promising option for improving the quality of care delivered. For example, using EHR tools has led to improved adherence to national guidelines in preventive and oncological care, opioid prescribing, and communication.10,12 Studies of EHR tools have often been focused on primary care and prevention, including in asthma, smoking cessation, and care for the elderly.10,12 No researchers to date have evaluated the effectiveness of integrating components of a longitudinal, condition-specific guideline.

Institutional adherence rates to components of the AAP guidelines were previously unknown. We expected comparability to the published rates ranging from 12% to 69% for individual components.6,9 We initiated this study to ascertain our adherence rates and investigate the impact of integrating select components of the AAP guidelines into the EHR. We expected that ease of access to recommendations would serve as a reminder, streamline the ordering process, and lead to improved adherence. We focused on adherence in NICUs and primary care centers to address outcomes throughout the life span.

Our purpose in this study was to evaluate the impact of our tools, integrated into the EHR tools, on guideline adherence for children with Down syndrome. Our project aim was to increase adherence to specific recommendations, genetic counseling, complete blood cell (CBC) count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study by 20% by December 2016 and to sustain the gains for 12 months afterward.

Nationwide Children’s Hospital (NCH) is a large, free-standing pediatric research center and health care system that delivers care to >1 million patients annually. NCH has a NICU at the main hospital and holds affiliation with multiple off-site NICUs at local birthing hospitals in non-NCH hospital systems. All off-site NICUs use the same EHR system, Epic, as that used at NCH. This quality improvement initiative involved a retrospective review to obtain baseline adherence followed by prospective tracking of patients with Down syndrome to manage the impact of the intervention. Inclusion criteria were a diagnosis of Down syndrome on the EHR problem list and a visit to NCH within the NICU, primary care centers, or genetics clinics from July 2015 to October 2017.

We used the Institute for Healthcare Improvement Model for Improvement as the framework for this effort. This included developing an aim statement with key drivers, executing plan-do-study-act cycles and/or interventions, and tracking improvement with control charts (Supplemental Fig 4). Our team consisted of a quality improvement analyst, an Epic analyst, a neonatologist and quality improvement expert, a geneticist, and parent representatives. The intervention consisted of incorporating select components of the AAP guidelines for Down syndrome into the EHR, including genetic counseling, CBC count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study. We selected these guidelines for the intervention because they are universally recommended for all children with Down syndrome of a given age. Completion of these components was readily accessible and measurable through chart review.

Two methods of EHR integration were created: (1) a best-practice advisory (BPA) prompt and (2) health maintenance record (HMR) tracking (Supplemental Fig 5). These EHR integrations became active and visible to ordering providers if a diagnosis of any form of Down syndrome was present on a patient’s problem list. EHR integrations had age-associated logic to prompt only recommendations for the patient’s current age. The BPA is an active pop-up window with a link to the current AAP guidelines. The BPA window remains open until the provider chooses “order” or “do not order” or closes the window. In a patient’s Epic chart, the HMR lists routine care, such as immunizations, with due dates and completion dates; this is visible in the top of the patient’s overview screen, known as the Snapshot. The HMR EHR integration includes a listing of AAP recommendations with a tracking of the due date for the next recommendation; this requires that physicians access this section of the EHR as might be done at a well-child visit.

We initially conducted a pilot quality improvement project in a genetics clinic and 3 high-volume NICUs to study the intervention, its impact on ordering, and adherence to genetic counseling. The pilot included the same EHR integrations as described above, and we queried the EHR by diagnosis code to identify all infants with Down syndrome born in 2013 or after. We also monitored the impact of the BPA on physician behavior (cancel and/or close window, order, or do not order) through Epic reports as a process measure. When monitoring the BPA alert rate, we found that initially, the BPA alerted multiple times during the same patient encounter. Because of concern that BPA fatigue would occur, we limited the BPA to alert only once per encounter; then, the EHR integration was expanded to primary care centers.

Both forms of EHR integration, the BPA, and the HMR were implemented in June 2016 at these select locations: primary care centers, NICUs, and the genetics clinic. This was a novel intervention; before this study, no components of the AAP guidelines were integrated into the EHR at NCH.

To study how closely recommendations are completed, our primary outcome measure was adherence. Adherence was operationally defined as the completion of each of 5 recommendations: genetic counseling, echocardiogram, CBC count and/or hemoglobin testing, thyrotropin testing, and sleep study. Measures are universally recommended, but adherence definitions differ by age (Supplemental Fig 6). For measures with specified time intervals, the date of visit was used. Adherence rates were calculated by dividing the number of completed recommendations for age by the number of total recommendations for age (Supplemental Fig 6). For example, a 3-year-old child with a completed echocardiogram and hemoglobin testing but no thyrotropin testing within 1 year of the visit would be scored as 1 of 2 because only hemoglobin and thyrotropin testing are recommended for this age group. Laboratories, referrals, or studies that were ordered but not completed were not counted in the numerator.

We also wanted to measure the BPA impact on provider behavior. To do so, the rate of BPA alerts per encounter and the number of orders placed as a direct result of the BPA were tracked.

Monthly reports of all patients with Down syndrome (International Classification of Diseases, 10th Revision code Q90.9) and an encounter at NCH were queried and filtered for relevant diagnoses and encounter locations. We continued to follow outcome reports of response to BPA messages, and provided order numbers were plotted in run charts. Monthly adherence percentages were plotted in p-charts. Adherence rates and data are presented at the encounter level to best capture the impact on ordering. We tracked the impact of this clinical change for >12 months. Centerline shifts were determined by using standard statistical process control chart rules.13,14 At our institution, we have standardized the use of the group of rules that have been published by the American Society for Quality for detecting special cause variation on control charts.15,16 

We compared adherence before and after EHR integration by type of EHR integration (BPA versus HMR) and encounter location. Lastly, we performed an age-based subanalysis using the following parameters: age <1 year, age 1 to <5 years, age 5 to <13 years, age 13 to <21 years, and age >21 years. We chose these age parameters to correspond with the age categorizations in the current AAP guidelines. Comparison by using χ2 analysis elicited differences before and after intervention implementation in June 2016. P < .01 was used as a cutoff for significance.

The institutional review board approved this project.

From July 2015 to October 2017, 2945 individuals with Down syndrome had 17 039 visits at NCH. Of these, 466 visits occurred in a location with EHR integration. During this time, some patients returned for follow-up visits. These 466 visits corresponded to 235 distinct individuals who received care in a location with EHR integration. Demographic information showed racial diversity, slight male predominance, and a distribution of ages with no significant difference (P > .01) when comparing the baseline and postintervention groups (Table 1).

TABLE 1

Demographic Traits of 235 Individuals With Down Syndrome and Visits at Select Sites Within NCH From July 2015 to October 2017

Baseline (N = 121)Postintervention (N = 114)
July 2015 to June 2016July 2016 to October 2017
N%N%
First visit in the study time frame 121 100 108 95 
Race     
 White 48 40 62 54 
 Black or African American 45 37 27 24 
 Not white, black, or African American 28 23 25 22 
Biological sex     
 Male 68 56 62 54 
 Female 53 44 52 46 
Age at visit, y     
 Average 6.4 — 5.6 — 
 SD 7.7 — 7.2 — 
Baseline (N = 121)Postintervention (N = 114)
July 2015 to June 2016July 2016 to October 2017
N%N%
First visit in the study time frame 121 100 108 95 
Race     
 White 48 40 62 54 
 Black or African American 45 37 27 24 
 Not white, black, or African American 28 23 25 22 
Biological sex     
 Male 68 56 62 54 
 Female 53 44 52 46 
Age at visit, y     
 Average 6.4 — 5.6 — 
 SD 7.7 — 7.2 — 

—, not applicable.

Monthly adherence for all components revealed a shift in adherence in September 2016 (Fig 1). The median baseline monthly adherence for select components before September 2016 was 61.6%, and after shift was detected, it significantly increased to 77.3% after the intervention (P < .001) and was sustained through the end of the study period. When analyzing by EHR integration type, adherence to the 4 recommendations implemented in the EHR in the HMR revealed significant improvement from a baseline adherence of 58.2% to 71.6% (P < .001), with a shift occurring in September 2016 (Fig 2). Tracking BPA impact, the immediate response of a provider to the BPA message, in a run chart revealed the placement of 1 to 8 orders per month as a direct result of EHR integration in June 2016 (Supplemental Fig 7). Location-specific analysis that was focused on primary care centers revealed an improved adherence from 58.8% to 75.5% (P < .001), with a shift occurring in September 2016 (Fig 3). In our data, the month of December 2016 was included in the analysis but appears distinct and is either special cause variation or near control limits (Figs 13). On the basis of standard statistical process control chart rules and institutional standards, our shifts in June 2016 are supported because either (1) there is only a single point outside control limits (Fig 1) or (2) a run of 12 of 14 occurred on the same side of the centerline (Figs 2 and 3).13,16 

FIGURE 1

Adherence rate to 5 select age-based AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

FIGURE 1

Adherence rate to 5 select age-based AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

Close modal
FIGURE 2

Adherence rate to the HMR implementation of 4 AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

FIGURE 2

Adherence rate to the HMR implementation of 4 AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

Close modal
FIGURE 3

Adherence rate at primary care clinics to 5 select age-based AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

FIGURE 3

Adherence rate at primary care clinics to 5 select age-based AAP guidelines for individuals with Down syndrome at NCH from July 2015 to October 2017. Solid lines indicate the process stage mean, which refers to the arithmetic mean for all points within that process stage; statistical rules indicate that there are 2 stable process stages, which are indicated by the shift in September 2016. Dashed lines indicate 2 stable process stages; dotted lines indicate the control limits (±3 SDs based on the process mean and number for that month).

Close modal

Pilot data of genetic counseling before NICU discharge revealed an improvement in adherence after the EHR integration (Supplemental Fig 8). From September 2016 to October 2017, 19 of 20 infants with Down syndrome met with genetics providers. Although this value did not reach statistical significance because of the small volume of infants with Down syndrome in these 3 NICUs, we were able to sustain having no discharges without genetic counseling for 51 weeks after the EHR integration. An investigation into the case in September 2017 revealed miscommunication between the genetics and neonatology providers, resulting in the neonatology team not placing a genetics referral.

Analyzing individual components increased adherence on all measures, with significant improvement occurring in adherence to genetic consultation and CBC count and/or hemoglobin testing; our shift in adherence was due to changes in overall adherence, not changes in a single measure (Table 2). A subanalysis by age revealed similar trends in improvement for most ages but did not reach statistical significance within all individual age groups (Table 3). The youngest ages had the greatest improvement; a comparison of pre- and postintervention rates for ages <5 years reached significance (P < .001). Improvement in ages ≥5 years did not reach statistical significance. Comparisons of adherence by type of EHR integration and encounter location revealed improvement after the intervention; significant improvement occurred in primary care centers, NICUs, and in recommendations prompted by both the BPA and HMR (Table 3).

TABLE 2

Age-Based Adherence Definitions and Rates by Individual Guidelines for Down Syndrome in 466 Visits to NCH

Adherence DefinitionBaseline: July 2015 to June 2016Postintervention: July 2016 to October 2017P
N = 167N = 299
n/N%n/N%
Echocardiogram Once ever for ages 0–12 mo 62/66 94 107/108 99 .05 
Genetics visit Once ever for ages 0–12 mo 37/66 56 97/108 90 <.001 
Hemoglobin Once at 0–6 mo with CBC then annually 88/167 51 206/299 69 <.001 
Thyrotropin Annually within last y for all children >6 mo 72/118 61 166/226 73 .02 
Sleep study Once for all children >4 y 33/65 51 78/119 66 .05 
Adherence DefinitionBaseline: July 2015 to June 2016Postintervention: July 2016 to October 2017P
N = 167N = 299
n/N%n/N%
Echocardiogram Once ever for ages 0–12 mo 62/66 94 107/108 99 .05 
Genetics visit Once ever for ages 0–12 mo 37/66 56 97/108 90 <.001 
Hemoglobin Once at 0–6 mo with CBC then annually 88/167 51 206/299 69 <.001 
Thyrotropin Annually within last y for all children >6 mo 72/118 61 166/226 73 .02 
Sleep study Once for all children >4 y 33/65 51 78/119 66 .05 
TABLE 3

Pre- and Postintervention Adherence Rates for Down Syndrome by Age, Location, and EHR Integration Type in 466 Visits to NCH

Preintervention: July 2015 to June 2016Postintervention: July 2016 to October 2017P
n/N%n/N%
By age, y      
 <5 201/314 64.0 462/546 84.6 <.001 
 5–<13 50/87 57.5 124/204 60.8 .60 
 13–<21 31/54 57.4 43/63 68.3 .23 
 >21 11/30 36.7 21/51 41.2 .69 
By hospital location      
 Primary care clinics 209/354 59.0 514/697 73.7 <.001 
 NICUs 35/63 55.6 82/93 88.2 <.001 
 Genetics clinic 48/65 73.8 58/74 78.4 .53 
By EHR integration type      
 BPA 37/66 56.1 97/109 90.0 <.001 
 HMR 243/416 58.4 528/755 69.9 <.001 
Preintervention: July 2015 to June 2016Postintervention: July 2016 to October 2017P
n/N%n/N%
By age, y      
 <5 201/314 64.0 462/546 84.6 <.001 
 5–<13 50/87 57.5 124/204 60.8 .60 
 13–<21 31/54 57.4 43/63 68.3 .23 
 >21 11/30 36.7 21/51 41.2 .69 
By hospital location      
 Primary care clinics 209/354 59.0 514/697 73.7 <.001 
 NICUs 35/63 55.6 82/93 88.2 <.001 
 Genetics clinic 48/65 73.8 58/74 78.4 .53 
By EHR integration type      
 BPA 37/66 56.1 97/109 90.0 <.001 
 HMR 243/416 58.4 528/755 69.9 <.001 

Adherence rate is the number of completed recommendations divided by the number of total recommendations.

EHR integration has been used for other conditions with published utility, but until now, it has not been used not for AAP guidelines. Because of our low adherence with the published AAP guidelines for Down syndrome, we integrated select components into the EHR and followed adherence to 5 measures in NICUs, primary care centers, and the genetics clinic over time.3,6 This novel intervention resulted in improved adherence over time from 61.6% to 77.3%; this improvement occurred for all components on subanalysis in the HMR and in primary care centers. Orders were placed directly from the BPA message, linking improved adherence directly to EHR integration.

Our baseline adherence rate to select AAP guidelines revealed a large opportunity for improvement. Adherence rates to some components were higher than published rates of genetic consultation adherence.6 Among the recommendations studied, the adherence rate for echocardiogram was highest, whereas rates for hemoglobin testing and sleep study by age 4 years were lowest. Disparity in adherence to individual components may be due to changes in recommendations over time; universal sleep study was not present in previous versions of the AAP guidelines for Down syndrome. Perceived risk may also contribute to adherence; stakeholders may view identifying congenital heart disease in an infant with Down syndrome as more important than other recommendations.

Analysis of age-based adherence revealed a disparity in baseline rates as well as a disparity in the degree of improvement. Increasing age correlated with decreased baseline adherence as well as less improvement in adherence after the EHR integration. With our emphasis being on genetic consultation, we compared this adherence to adherence to 4 other select components. Because adherence to genetic consultations was recommended through the BPA and other recommendations were via HMR, EHR integration type is a proxy for genetic versus nongenetic components. Both types of EHR integration (BPA versus HMR) showed improved adherence; greater increases in adherence occurred with active BPA alerts compared with passive HMR. Tracking HMR-implemented recommendations revealed a shift in adherence that is consistent with the shift in overall adherence; passively listing components in the HMR significantly improved adherence.

Adherence improved in all 3 location types. Within the 3 target NICUs, improved genetic consultation adherence led to a period of ∼1 year when all infants born had genetic counseling before discharge. Primary care centers had a shift that was consistent with the overall adherence shift. On the basis of our results, we state that encounter location, EHR integration type, and genetic consultation emphasis do not account for the lower adherence in older individuals with Down syndrome. Many of the specific recommendations for the individual components studied remain the same across later ages; of the 5 studied, all individuals >4 years of age are measured on the same components. Indeed, disparities in health care for adults with disabilities have been seen in Ohio.17 Although many Down syndrome clinics are established nationally, these tend to emphasize the pediatric population.18 After the age of 21 years, specific guidelines for people with Down syndrome are lacking.

Studying select clinics and locations may limit the generalizability of our results because of selection bias; our sample accounts for only a fraction of the visits to the hospital system. However, we chose to include primary care clinics so that we could most accurately reflect adherence in the general population. Our initial adherence rate was similar to a published rate from a review of outpatient pediatrics clinics, suggesting similarity to a general outpatient pediatric clinic population.6 Because the HMR is frequently accessed when reviewing a patient’s record for routine care, primary care clinics fit with the target of our intervention method. Future researchers could expand to the larger hospital system to determine if these interventions successfully impact additional patients. Expanding the project to include multiple sites would also improve generalizability and account for secular trends and the limitations of a single-institution study.

Our initial pilot project of genetic counseling adherence included multiple interventions, including genetics appointment availability, communication with the laboratory, and education of community stakeholders. These steps can confound our pilot genetic consultation improvement, but similar improvements were seen in other locations and for other components that were not impacted by our pilot project. Additional limitations could include inaccessibility of medical documentation at outside institutions, changes in medical documentation from paper notes to electronic medical records, and counting individual patients more than once if seen repeatedly at 1 of these locations. One month appeared to be distinct and had the smallest number of visits per month; when we focused attention on the context of this month, we did not identify an explanation for the lower adherence.

EHR integration is a useful tool for improving adherence to the health supervision guidelines for Down syndrome. Interventions are transportable and translatable: BPAs can be shared with other organizations via Epic’s Community Library. Those at other facilities can see the build that was completed and build similar code at their organizations. Interventions are sustainable and provide both instant access to order screens and a direct link to the AAP guidelines. In our study, EHR integration for primary care centers was performed in isolation, without additional awareness efforts or physician education regarding the use of the BPA or HMR. Guideline adherence, including those listed in the HMR, improved. With future revisions of guidelines, modifying EHR integrations can enable a rapid spread of physician education and communication.

However, EHR integration is not without limitations; its use to improve adherence to guidelines is not a perfect solution. EHR integration necessitates careful thought and planning to be effective and may need continued improvement.19 EHR prompts to document smoking status resulted in only a modest improvement in adherence.20 EHR integration reminders can be further improved with the addition of an employed care manager.12 In our pilot project related to genetic consultation, it was essential that we could be responsive (that is, we had identified appointment times and an available consult service to be able to accommodate orders). Because our EHR integration includes an active pop-up prompting ordering, multiple and repeated BPAs could be an annoyance and lead to BPA fatigue. Adding the diagnosis of Down syndrome to the problem list prompted our EHR integrations requiring that providers consistently update this list; if not done, patients would be missed in our study, which relied on International Classification of Diseases, 10th Revision codes to identify patients. EHR integration does not account for patient individuality.

No published researchers have directly linked guideline adherence to health outcomes in Down syndrome. Increased adherence to the 2007 US asthma guidelines was linked to “in-control” asthma and higher quality-of-life scores.21 The implementation of guidelines has led to improved health care outcomes for breastfeeding,22,24 safe sleep,25,27 diabetic ketoacidosis,28 acute myocardial infarction,29,30 and colorectal cancer.31,32 Systematic literature review of heart failure studies in which researchers directly link process outcomes to clinical outcomes show that many lack statistical power.33 Additional quality improvement work for the AAP guidelines for Down syndrome is needed and should include broader measures of health beyond adherence.

Isolated EHR integration of 5 select recommendations for Down syndrome improved adherence to components of “Health Supervision for Children With Down Syndrome.” Future studies could be focused on the underlying cause of nonadherence, its influence on families’ experiences at diagnosis, the utility of other interventions, and the impact of guideline adherence on health.

     
  • AAP

    American Academy of Pediatrics

  •  
  • BPA

    best-practice advisory

  •  
  • CBC

    complete blood cell

  •  
  • EHR

    electronic health record

  •  
  • HMR

    health maintenance record

  •  
  • NCH

    Nationwide Children’s Hospital

Dr Santoro conceptualized and designed the study, collected data, completed statistical analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Bartman provided feedback on the study design, interpreted statistical analyses, and reviewed the manuscript; Dr Cua provided feedback on the data collection and interpretation of statistical analyses and reviewed and revised the manuscript; Ms Lemle collected data, conducted the initial analyses, and reviewed and revised the manuscript; Dr Skotko designed the data collection definitions, coordinated and supervised data collection, and critically reviewed the manuscript; and all authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.

FUNDING: No external funding.

Appreciation is given to Senita Francis for creating this BPA as well as to groups within NCH for allowing this project to be implemented. Thank you to the Down Syndrome Association of Central Ohio and its team members, including Dr Gary Snyder at Riverside Methodist Hospital’s NICU, Dr Carl Backes Jr at The Ohio State University NICU, and laboratory genetic counselors at NCH. Appreciation is also given to Dr Robert Hopkin at Cincinnati Children’s Hospital Medical Center for his mentorship and assistance with the initial conceptualization of the EHR integration of established guidelines.

1
de Graaf
G
,
Buckley
F
,
Skotko
BG
.
Estimates of the live births, natural losses, and elective terminations with Down syndrome in the United States.
Am J Med Genet A
.
2015
;
167A
(
4
):
756
767
[PubMed]
2
Bull
MJ
;
Committee on Genetics
.
Health supervision for children with Down syndrome.
Pediatrics
.
2011
;
128
(
2
):
393
406
[PubMed]
3
Fergeson
MA
,
Mulvihill
JJ
,
Schaefer
GB
, et al
.
Low adherence to national guidelines for thyroid screening in Down syndrome.
Genet Med
.
2009
;
11
(
7
):
548
551
[PubMed]
4
Cohen
WI
.
Current dilemmas in Down syndrome clinical care: celiac disease, thyroid disorders, and atlanto-axial instability.
Am J Med Genet C Semin Med Genet
.
2006
;
142C
(
3
):
141
148
[PubMed]
5
Jensen
KM
,
Taylor
LC
,
Davis
MM
.
Primary care for adults with Down syndrome: adherence to preventive healthcare recommendations.
J Intellect Disabil Res
.
2013
;
57
(
5
):
409
421
[PubMed]
6
Santoro
SL
,
Martin
LJ
,
Pleatman
SI
,
Hopkin
RJ
.
Stakeholder buy-in and physician education improve adherence to guidelines for Down syndrome.
J Pediatr
.
2016
;
171
:
262
268.e1–268.e2
7
Santoro
SL
,
Yin
H
,
Hopkin
RJ
.
Adherence to symptom-based care guidelines for Down syndrome.
Clin Pediatr (Phila)
.
2017
;
56
(
2
):
150
156
[PubMed]
8
Skotko
BG
,
Davidson
EJ
,
Weintraub
GS
.
Contributions of a specialty clinic for children and adolescents with Down syndrome.
Am J Med Genet A
.
2013
;
161A
(
3
):
430
437
[PubMed]
9
Santoro
SL
,
Jacobson
T
,
Lemle
S
,
Bartman
T
.
Integrating a geneticist in a multidisciplinary clinic for Down syndrome increases commitment to genetic counseling.
Pediatr Qual Saf
.
2017
;
2
(
5
):
e039
10
Bernens
JN
,
Hartman
K
,
Curley
B
, et al
.
Assessing the impact of a targeted electronic medical record intervention on the use of growth factor in cancer patients.
J Community Support Oncol
.
2015
;
13
(
3
):
113
116
[PubMed]
11
Trafton
JA
,
Martins
SB
,
Michel
MC
, et al
.
Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain.
Implement Sci
.
2010
;
5
:
26
[PubMed]
12
Loo
TS
,
Davis
RB
,
Lipsitz
LA
, et al
.
Electronic medical record reminders and panel management to improve primary care of elderly patients.
Arch Intern Med
.
2011
;
171
(
17
):
1552
1558
[PubMed]
13
Provost
LP
,
Murray
SK
.
The Health Care Data Guide: Learning From Data for Improvement
. 1st ed.
San Francisco, CA
:
Jossey-Bass
;
2011
14
Langley
GJ
.
The Improvement Guide: A Practical Approach to Enhancing Organizational Performance
. 2nd ed.
San Francisco, CA
:
Jossey-Bass
;
2009
15
American Society for Quality (ASQ)
. 2018. Learn about quality: control chart. Available at: http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/control-chart.html. Accessed May 11, 2018
16
Tague
NR
.
The Quality Toolbox
. 2nd ed.
Milwaukee, WI
:
ASQ Quality Press
;
2005
17
Prokup
JA
,
Andridge
R
,
Havercamp
SM
,
Yang
EA
.
Health care disparities of Ohioans with developmental disabilities across the lifespan.
Ann Fam Med
.
2017
;
15
(
5
):
471
474
[PubMed]
18
Global Down Syndrome Foundation
. Down syndrome medical care centers list. 2017. Available at: www.globaldownsyndrome.org/research-medical-care/medical-care-providers/. Accessed December 8, 2017
19
Price
M
,
Davies
I
,
Rusk
R
,
Lesperance
M
,
Weber
J
.
Applying STOPP guidelines in primary care through electronic medical record decision support: randomized control trial highlighting the importance of data quality.
JMIR Med Inform
.
2017
;
5
(
2
):
e15
[PubMed]
20
Bentz
CJ
,
Bayley
KB
,
Bonin
KE
, et al
.
Provider feedback to improve 5A’s tobacco cessation in primary care: a cluster randomized clinical trial.
Nicotine Tob Res
.
2007
;
9
(
3
):
341
349
[PubMed]
21
Yawn
BP
,
Wollan
PC
,
Rank
MA
,
Bertram
SL
,
Juhn
Y
,
Pace
W
.
Use of asthma APGAR tools in primary care practices: a cluster-randomized controlled trial.
Ann Fam Med
.
2018
;
16
(
2
):
100
110
[PubMed]
22
Horwood
LJ
,
Fergusson
DM
.
Breastfeeding and later cognitive and academic outcomes.
Pediatrics
.
1998
;
101
(
1
). Available at: www.pediatrics.org/cgi/content/full/101/1/e9
[PubMed]
23
Chantry
CJ
,
Howard
CR
,
Auinger
P
.
Full breastfeeding duration and associated decrease in respiratory tract infection in US children.
Pediatrics
.
2006
;
117
(
2
):
425
432
[PubMed]
24
Duijts
L
,
Jaddoe
VW
,
Hofman
A
,
Moll
HA
.
Prolonged and exclusive breastfeeding reduces the risk of infectious diseases in infancy.
Pediatrics
.
2010
;
126
(
1
). Available at: www.pediatrics.org/cgi/content/full/126/1/e18
[PubMed]
25
Moon
RY
,
Calabrese
T
,
Aird
L
.
Reducing the risk of sudden infant death syndrome in child care and changing provider practices: lessons learned from a demonstration project.
Pediatrics
.
2008
;
122
(
4
):
788
798
[PubMed]
26
Price
SK
,
Hillman
L
,
Gardner
P
,
Schenk
K
,
Warren
C
.
Changing hospital newborn nursery practice: results from a statewide “Back to Sleep” nurses training program.
Matern Child Health J
.
2008
;
12
(
3
):
363
371
[PubMed]
27
Moon
RY
;
Task Force on Sudden Infant Death Syndrome
.
SIDS and other sleep-related infant deaths: evidence base for 2016 updated recommendations for a safe infant sleeping environment.
Pediatrics
.
2016
;
138
(
5
):
e20162940
[PubMed]
28
White
PC
,
Dickson
BA
.
Low morbidity and mortality in children with diabetic ketoacidosis treated with isotonic fluids.
J Pediatr
.
2013
;
163
(
3
):
761
766
[PubMed]
29
Soumerai
SB
,
McLaughlin
TJ
,
Spiegelman
D
,
Hertzmark
E
,
Thibault
G
,
Goldman
L
.
Adverse outcomes of underuse of beta-blockers in elderly survivors of acute myocardial infarction.
JAMA
.
1997
;
277
(
2
):
115
121
[PubMed]
30
Goldberger
JJ
,
Bonow
RO
,
Cuffe
M
, et al;
OBTAIN Investigators
.
Effect of beta-blocker dose on survival after acute myocardial infarction.
J Am Coll Cardiol
.
2015
;
66
(
13
):
1431
1441
[PubMed]
31
Mandel
JS
,
Bond
JH
,
Church
TR
, et al
.
Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study.
N Engl J Med
.
1993
;
328
(
19
):
1365
1371
[PubMed]
32
US Preventive Services Task Force
.
Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement.
Ann Intern Med
.
2008
;
149
(
9
):
627
637
[PubMed]
33
Shanbhag
D
,
Graham
ID
,
Harlos
K
, et al
.
Effectiveness of implementation interventions in improving physician adherence to guideline recommendations in heart failure: a systematic review.
BMJ Open
.
2018
;
8
(
3
):
e017765
[PubMed]

Competing Interests

POTENTIAL CONFLICT OF INTEREST: Dr Santoro is a board-certified pediatrician who serves on the governing board and medical advisory committee of the Down Syndrome Association of Central Ohio. Dr Skotko has a sister with Down syndrome and occasionally consults on the topic of Down syndrome through the Gerson Lehrman Group; receives remuneration from Down syndrome nonprofit organizations for speaking engagements and associated travel expenses; receives annual royalties from Woodbine House, Inc, for the publication of his book, Fasten Your Seatbelt: A Crash Course on Down Syndrome for Brothers and Sisters; is occasionally asked to serve as an expert witness for legal cases in which Down syndrome is discussed; serves in a unpaid capacity on the Honorary Board of Directors for the Massachusetts Down Syndrome Congress, the Board of Directors for the Band of Angels Foundation, and the Professional Advisory Committee for the National Center for Prenatal and Postnatal Down Syndrome Resources; and within the past 2 years, he has received research funding from F. Hoffmann-La Roche and Transition Therapeutics to conduct clinical trials on study drugs for people with Down syndrome; and Drs Cua and Bartman and Ms Lemle have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data