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Targeted screening for pediatric conditions with the CHICA system

Aaron E Carroll , Paul G Biondich , Vibha Anand , Tamara M Dugan , Meena E Sheley , Shawn Z Xu , Stephen M Downs
DOI: http://dx.doi.org/10.1136/amiajnl-2011-000088 485-490 First published online: 1 July 2011


Objective The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICA's ability to screen patients for disorders that have validated screening criteria—specifically tuberculosis (TB) and iron-deficiency anemia.

Design Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders.

Results 1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0).

Limitations It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them.

Conclusions Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.

  • Tuberculosis
  • iron-deficiency anemia
  • screening
  • computer decision-support system
  • visualization of data and knowledge
  • knowledge representations
  • communications
  • networking methods
  • mobility
  • uncertain reasoning and decision theory
  • languages
  • computational methods
  • advanced algorithms
  • methods for integration of information from disparate sources
  • distributed systems
  • agents
  • software engineering: architecture
  • data models
  • automated learning
  • developing/using clinical decision support (other than diagnostic) and guideline systems
  • discovery
  • text and data mining methods


Well-child care is a service fundamental to pediatrics, representing nearly half of healthcare visits made by children in the USA.1 These visits are designed to encompass a variety of health-promoting and disease-preventing services, including growth and development assessments, screens for subtle or asymptomatic disease, and delivery of services such as immunizations.2 Because preventive service delivery to children is neither procedure-oriented nor hospital-based, the relative cost per visit is small compared to other medical problems encountered in the healthcare system. However, the 19.8 million children under the age of five in the USA should have each received 13 well-child visits by the time they reach school age.2 ,3 Therefore, the magnitude and potential impact of childhood preventive services delivery is large.

The importance of preventive care has been widely emphasized, and a growing body of preventive services is routinely proposed by various expert panels and professional organizations.2 ,46 They range all the way from newborn metabolic and hearing screening to counseling 5 year olds about head-injury prevention through bike-helmet use. As these recommendations proliferate, a growing body of research continues to document the benefits of these preventive services on such diverse topics as injury prevention,7 ,8 childhood tuberculosis,9 childhood literacy,10 ,11 healthy sleep habits,12 ,13 and infant iron-deficiency.14 ,15

Despite the widely accepted importance of preventive services, mounting evidence suggests that the provision of these services is suboptimal, even for services for which there is ample evidence and broad support. Among a Medicaid population for example, only one-fifth of children received preventive and developmental services that met a basic threshold of quality.16 A national survey of parents found that more than 94% of parents reported one or more unmet needs for parenting guidance, education, or screening by pediatric clinicians of recommended services.17 Overall, rates of delivery for basic preventive services are typically <50%.17 Some would argue that these figures are generous, as studies indicate that most clinicians overestimate the level of preventive care they provide, especially screening tests that they give their patients.18 ,19

When physicians are asked to describe the challenges in providing preventive services, they are quick to provide a convincing list of barriers commonly faced in pediatric settings.20 In an attempt to provide a framework which categorizes these barriers into groups, Cabana et al performed a meta-analysis on 76 articles which described one or more problems physicians faced while adhering to care guidelines.21 Three barriers most consistently identified in this study relate to time constraints, problems inherent in case finding and prioritization, and a lack of clinician self-efficacy.

Much of the time required to provide preventive care services is spent attempting to assess risk, as care guidelines are often lengthy and complex, and may not apply to the individual circumstances of each patient. According to the US Preventive Services Task Force, when care givers place the greatest emphasis on understanding each patient's risk profile, they can significantly reduce the number of unnecessary interventions.4 As a result, many guidelines attempt to define an explicit, focused list of historical and demographic risk factors to facilitate rapid recognition of risk. To further reduce provider work burden, many of these risk factors are bundled into standardized screening instruments which can be completed by patients or their families.2225 However, providers are still left to determine who receives each of these instruments, and must also score and/or interpret the results.

A great deal of research indicates that clinical reminders provided to physicians and other care givers at the point of patient care are superior to other methods of affecting clinical practice. However, reminders are typically delivered today by computerized physician order-entry systems or inpatient noting systems.2631 Unfortunately, for many outpatient preventive services, a reminder at the time of note writing or order entry is often too late, as these events frequently take place after the physician has completed the visit. ‘Just-in-time’ information delivery requires that a reminder be delivered at the time the physician is making a decision, and this is often while they are conversing with a patient. Computers within exam rooms may not be a satisfactory solution, as they can be expensive and susceptible to damage by curious pediatric patients. Computers can also slow the patient encounter and negatively impact the content of physician–patient communications.32 In fact, at our institution, which houses one of the most successful electronic medical record systems in the world, pediatricians have long been resistant to the introduction of computers in their clinics for these reasons.

Child Health Improvement through Computer Automation (CHICA) system

CHICA is a decision-support and electronic medical record system for pediatric health maintenance and disease management. CHICA also serves as a front end for data exchange with the Regenstrief Medical Record System.33 It can also work as a standalone application or be coupled with any other clinical information system. CHICA's primary user interface consists of two sheets of paper that collect handwritten responses to dynamically generated questions and clinical reminders while easily integrating into clinic workflow.34 ,35 These forms are created by CHICA and tailored to the individual patient. The forms, called adaptive turnaround documents (ATDs), are scanned, and data are read by optical character recognition and optical mark recognition software. CHICA uses a library of Arden Syntax36 rules that utilize data from the RMRS and CHICA record systems to determine what information should be printed on each ATD. CHICA also uses a global prioritization scheme to determine which information is most relevant for inclusion on the printouts.37 This effectively constrains the number of topics that CHICA recommends be addressed to a feasible number for any given patient encounter.

When a patient checks into the clinic, our clinic appointment system sends a registration HL7 message to alert CHICA to begin generating the first of two ATDs. This first form, called the ‘prescreening’ form (PSF), captures data from both clinic staff and parents before the patient is seen by a physician. This form has a section for nurses to enter morphometrics, vital signs, and other data. In addition, the PSF contains 20 questions selected by CHICA to ask a child's family (figure 1). These 20 questions are typically answered by the patient's parent while in the waiting room of the clinic. Data obtained through this form are merged with previously existing data and analyzed to generate a second ATD, known as the ‘provider worksheet’ (PWS). This PWS is tailored to the individual patient and contains up to six reminders which are based on the merged data of the PSF and existing electronic medical records (figure 2). Each reminder contains a ‘stem,’ introducing the reason for the prompt and between one and six ‘leaves' which consist of check boxes allowing the physician to document their responses to the prompt. Some answers to questions on the PSF might also result in CHICA printing a ‘just in time’ handout (JIT). These JITs can provide additional advice to the physician (eg, a depression screening tool) or to the patient (eg, an asthma action plan or educational handout). The JIT prints at the same time as the PWS and is placed on the chart for the physician's use during the encounter.

Figure 1

Child Health Improvement through Computer Automation (CHICA) prescreening form (tuberculosis screening questions have been highlighted).

Figure 2

Child Health Improvement through Computer Automation (CHICA) physician worksheet.


The purpose of this study was to explore CHICA's ability to implement screening guidelines. We specifically wanted to see how CHICA would affect the screening of patients for the following two conditions:

  1. Iron-deficiency anemia38

    • –The American Academy of Pediatrics recommends blood hemoglobin or hematocrit tests for all 9–15-month-old children who demonstrate nutritional risk factors such as drinking cow's milk before 1 year of age, consuming a low-iron diet, drinking low-iron formula, or being born prematurely.39

  2. Tuberculosis40

    • –Guidelines also exist for the placement of purified protein derivative tests for children. Risk factors indicating that a child should be screened include the following: exposure to someone with tuberculosis (TB), travel to a high risk country, someone else in the home having traveled to a high risk country, or exposure to anyone who is themselves at high risk for TB.41


Our first step was to modify the CHICA system in order to add in rules for both iron-deficiency anemia and tuberculosis screening. A specific rule set was created for the CHICA system to improve screening for both iron-deficiency anemia and tuberculosis. We adapted questions from American Academy of Pediatrics guidelines for the PSF that asked parents about potential risk factors for these conditions (table 1). These questions were coded in such a way that they would appear a specified number of times over a certain age period (table 1). We also created three PWS prompts for each condition. The first was generic, and would appear if no information was available from the PSF or if no PSF questions were asked of the parents. The generic prompt asked the physician to assess the child's risk factors for iron-deficiency or TB. The physician could indicate identified risk(s) by checking the corresponding box(es) on the prompt. The second was tailored, and would fire if a risk factor was identified on the PSF. The ‘stem’ of the prompt would include the risk factor identified on the PSF. The third was a follow-up prompt and would remind or instruct the physician to follow-up on screening steps taken in previous visits.

View this table:
Table 1

Prescreening form prompting for intervention groups

ConditionPrompt textAge rangeHow often
Iron-deficiency anemiaHave you given Jenny cow's milk to drink?0–12 monthsOnce every 60 days
Iron-deficiency anemiaAre you feeding Jenny low-iron formula?0–12 monthsOnce every 30 days
Iron-deficiency anemiaDoes Jenny get fed two or more servings of the following foods daily: iron-fortified cereals, fruits, vegetables, juices, or puréed meats?6–15 monthsOnce every 60 days
Iron-deficiency anemiaWas Jenny's birth weight less than five pounds and eight ounces (2500 g)?0–2 yearsOnce
TuberculosisHas Jenny spent more than a week in Asia, Africa, Latin America, or Eastern Europe?11 months–11 yearsOnce a year
TuberculosisHas Jenny been exposed to anyone with tuberculosis disease?11 months–11 yearsOnce a year
TuberculosisHas anyone in Jenny's home recently spent more than a week in Asia, Africa, Latin America, or Eastern Europe?11 months–11 yearsOnce a year
TuberculosisDoes Jenny spend time with anyone who has been in jail or a shelter, uses illegal drugs, or has HIV?11 months–11 yearsOnce a year


All children age 0–11 years seen in our main primary care clinic between February 1, 2008 and July 15, 2009 were randomized into an intervention and a control group for each of our two modules. Randomization was done automatically by the CHICA system at the time of patient registration. Each year, this clinic sees about 1200 children in that age group. In the control group, parents were not asked screening questions about TB or iron-deficiency risk in the PSF, and the physician received only the generic PWS prompt, as no questions ever appeared on the PSF. Therefore, any screening that occurred would have to be initiated by the physician as part of routine care.

In the intervention group, parents were asked about TB and iron-deficiency risk on the PSF. If risk factors were identified, the physician received the tailored prompt. If the risk factors were absent, no PWS prompt was printed for the physician. If PSF questions were left unanswered, the physician received the generic prompt.

As patients are already familiar with the use of CHICA in our clinic system, no additional instructions were given to patients in the control or intervention group as to how to fill out the forms; nor were physicians oriented to any new prompts for either TB or iron-deficiency anemia. In general, we have found that patients are extremely thorough in completing the PSF questions.42


Differences in measurable covariates were independently analyzed using χ2 analysis. If differences between groups were significant, we included them in further multivariable regressions. We hypothesized that the use of the PSF would significantly improve screening practices for both TB and iron-deficiency. We used logistic regression to test these hypotheses. All calculations were performed using the STATA 9.0 statistical package. All methods for this study were approved by the Indiana University School of Medicine Institutional Review Board.


Over the course of this study, 2239 patients were randomized. The control group had 1123 participants, and the intervention group had 1116 participants. Patients enrolled were about equally split between males and females, and were mostly Hispanic (45%) or African–American (36%). Summary statistics on the population are listed in table 2.

View this table:
Table 2

Summary statistics study participants

Control N (%)Intervention N (%)Totalp Value
Sex 0.96
   Males581 (52)578 (52) 1159
   Females542 (48)538 (48) 1080
Race 0.22
   American Indian0 (0)2 (0) 2
   Asian Pacific27 (2)21 (2) 48
   Black397 (36)394 (36) 791
   Hispanic510 (46)497 (45) 1007
   White152 (14)182 (16) 334
Patients with at least this many visits 0.94
   11123 (100)1116 (100) 2239
   2943 (84)955 (86) 1898
   3798 (71)836 (75) 1634
   4630 (56)670 (60) 1300
   5467 (42)487 (44) 954
   6296 (26)321 (29) 617
   >6177 (16)178 (16) 355

Iron-deficiency anemia

Differences in rates of detection of risk factors for iron-deficiency anemia are listed in table 3. Although slightly more people reported early cow's milk use in the control group (1.1% vs 1.5%), more people in the intervention group reported a low birth weight (3.0% vs 1.1%), low-iron diet (3.9% vs 1.0%), and the use of low-iron formula (13.4% vs 1.2%). Overall, significantly more people reported positive risk factors in the intervention group than in the control group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). Three children in the intervention group (0.3%) had positive CBCs vs none in the control group, although this difference was not statistically significant (p=0.08).

View this table:
Table 3

Differences in positive screening rates for iron-deficiency-anemia risk factors

NoPercentagep Value
Early cow's milk<0.36
Low birth weight<0.007
Low-iron diet<0.001
Low-iron formula<0.001
Any risk factor<0.001


Differences for screening rates for tuberculosis are listed in table 4. In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Although slightly more cases of exposure to someone with tuberculosis were detected in the control group (0.2% vs 0.5%), more cases of travel to a high-risk country were found in the intervention group (0.4% vs 0.3%), as were cases of contact with someone who had traveled to a high-risk country (0.8% vs 0.2%), and living with someone who is at high risk (0.9% vs 0.6%). Again, there were significantly higher detection rates of positive risk factors in the intervention group than in the control group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0). Two children in the intervention group (0.2%) had positive purified protein derivatives versus none in the control group, although this difference was not statistically significant (p=0.16).

View this table:
Table 4

Differences in positive screening rates for tuberculosis risk factors

NoPercentagep Value
High-risk travel<0.71
Exposure to tuberculosis<0.18
Contact with high-risk traveler<0.06
High-risk contact<0.47
Any risk factor<0.05


With so many recommendations and guidelines available, it is impossible for a clinician to adhere to them all. This is especially true with respect to screening. Many screening tests are risk-based and not meant to be universally performed. Instead, physicians are left to determine who should or should not have a further workup or procedure. This can be a time-consuming process and therefore is often neglected. In this study, we demonstrated that the CHICA system performs well in automatically assessing risk directly from parents and patients to determine who should receive risk-based screening for tuberculosis and iron-deficiency anemia.

In previous studies, we showed that the CHICA PSF is accepted well by parents and integrates easily into the primary care workflow.35 In this study, we add to that knowledge by showing that questions aimed at screening for conditions where targeted screening is needed can enhance the process by collecting data directly from parents. Many more patients were picked up in the intervention than the control group for both a more common condition, iron-deficiency anemia, and a rarer one, tuberculosis. When risks are automatically identified, clinicians can then be alerted to the need for a test or procedure in a manner that will increase compliance with recommendations and guidelines.

As with any research, this study has limitations that warrant further consideration. It is possible that patients who screened positive by CHICA might still not warrant further workup. In other words, it is possible that we may have more positive screens without improving outcomes. Assessing these outcomes is beyond the scope of this study. However, the guidelines are based on studies that have evaluated the questions we used as sensitive and specific, and there is no reason to believe that parents misunderstood them. Further, there is no guarantee that screening results end in treatment or cure, which is the ultimate goal. However, screening properly, and in a non-intrusive fashion, is a necessary first step, and this study confirms that the CHICA system performs that task well. A factorial design, or a study that also had a control group where the CHICA system was not utilized at all, may have been a more powerful design. However, feasibility issues concerning randomization at the time the study began prevented us from studying the interventions in this way.

Integrating decision support into clinical care, at the point of care, is a difficult process. The CHICA system is built on an open-source platform, using national health-information technology standards,43 and has been studied and adjusted at various points along its development. At this stage, we have confirmed the success of the PSF, filled out by parents, in screening patients for two conditions. We have added many more conditions and surveillance modules to our system over time, and we believe that CHICA will continue to screen patients well. Moreover, we are engaged in studies of the physician worksheet to determine if it can close the loop properly, by improving the steps clinicians must take to provide care that meets recommendations.

Competing interests


Ethics approval

Indiana University Institutional Review Board.

Provenance and peer review

Not commissioned; externally peer reviewed.


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