Appl Clin Inform 2015; 06(03): 454-465
DOI: 10.4338/ACI-2014-09-RA-0084
Research Article
Schattauer GmbH

Computer decision support changes physician practice but not knowledge regarding autism spectrum disorders

N.S. Bauer
1   Indiana University School of Medicine, Department of Pediatrics, Section of Children’s Health Services Research, Indianapolis, Indiana, USA
3   Regenstrief Institute for Healthcare, Indianapolis, Indiana, USA
,
A.E. Carroll
2   Indiana University School of Medicine, Department of Pediatrics, Section of Pediatric and Adolescent Comparative Effectiveness Research, Indianapolis, Indiana, USA
3   Regenstrief Institute for Healthcare, Indianapolis, Indiana, USA
,
C. Saha
4   Indiana University School of Medicine, Department of Biostatistics
,
S.M. Downs
1   Indiana University School of Medicine, Department of Pediatrics, Section of Children’s Health Services Research, Indianapolis, Indiana, USA
3   Regenstrief Institute for Healthcare, Indianapolis, Indiana, USA
› Author Affiliations
Further Information

Publication History

received: 09 February 2015

accepted in revised form: 08 June 2015

Publication Date:
19 December 2017 (online)

Summary

Objective: To examine whether adding an autism module promoting adherence to clinical guidelines to an existing computer decision support system (CDSS) changed physician knowledge and self-reported clinical practice.

Methods: The CHICA (Child Health Improvement through Computer Automation) system, a CDSS, was enhanced with a module to improve management of autism in 2 of the 4 community pediatric clinics using the system. We examined the knowledge and beliefs of pediatric users using cross-sectional surveys administered at 3 time points (baseline, 12 months and 24 months post-implementation) between November 2010 and January 2013. Surveys measured knowledge, beliefs and self-reported practice patterns related to autism.

Results: A total of 45, 39, and 42 pediatricians responded at each time point, respectively, a 95-100% response rate. Respondents’ knowledge of autism and perception of role for diagnosis did not vary between control and intervention groups either at baseline or any of the two post-intervention time points. At baseline, there was no difference between these groups in rates in the routine use of parent-rated screening instruments for autism. However, by 12 and 24 months post-implementation there was a significant difference between intervention and control clinics in terms of the intervention clinics consistently screening eligible patients with a validated autism tool. Physicians at all clinics reported ongoing challenges to community resources for further work-up and treatment related to autism.

Conclusions: A CDSS module to improve primary care management of ASD in pediatric practice led to significant improvements in physician-reported use of validated screening tools to screen for ASDs. However it did not lead to corresponding changes in physician knowledge or attitudes.

Citation: Bauer NS, Carroll AE, Saha C, Downs SM. Computer decision support changes physician practice but not knowledge regarding autism spectrum disorders. Appl Clin Inform 2015; 6: 454–465

http://dx.doi.org/10.4338/ACI-2014-09-RA-0084

 
  • References

  • 1 Baio J. Prevalence of Autism Spectrum Disorders-Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. MMWR Surveill Summ 2012; 61 SS03 1-19.
  • 2 Dosreis S, Weiner CL, Johnson L, Newschaffer CJ. Autism spectrum disorder screening and management practices among general pediatric providers. J Dev Behav Pediatr 2006; 27 (02) S88-94.
  • 3 Osborne LA, Reed P. Parents’ perceptions of communication with professionals during the diagnosis of autism. Autism 2008; 12 (03) 309-324.
  • 4 Shattuck PT, Durkin M, Maenner M, Newschaffer C, Mandell DS, Wiggins L, Lee LC, Rice C, Giarelli E, Kirby R, Baio J, Pinto-Martin J, Cuniff C. Timing of identification among children with an autism spectrum disorder: findings from a population-based surveillance study. J Am Acad Child Adolesc Psychiatry 2009; 48 (05) 474-483.
  • 5 American Academy of Pediatrics: The pediatrician’s role in the diagnosis and management of autistic spectrum disorder in children. Pediatrics 2001; 107 (Suppl. 05) 1221-1226.
  • 6 Johnson CP, Myers SM. Identification and evaluation of children with autism spectrum disorders. Pediatrics 2007; 120 (05) 1183-1215.
  • 7 Robins DL. Screening for autism spectrum disorders in primary care settings. Autism 2008; 12 (05) 537-556.
  • 8 Blumenthal D, Causino N, Chang YC, Culpepper L, Marder W, Saglam D, Stafford R, Starfield B. The duration of ambulatory visits to physicians. The Journal of family practice 1999; 48 (04) 264-271.
  • 9 Belamarich PF, Gandica R, Stein RE, Racine AD. Drowning in a sea of advice: pediatricians and American Academy of Pediatrics policy statements. Pediatrics 2006; 118 (04) e964-978.
  • 10 Allen SG, Berry AD, Brewster JA, Chalasani RK, Mack PK. Enhancing developmentally oriented primary care: an Illinois initiative to increase developmental screening in medical homes. Pediatrics 2010; 126 (03) S160-164.
  • 11 Zuckerman KE, Mattox K, Donelan K, Batbayar O, Baghaee A, Bethell C. Pediatrician identification of Latino children at risk for autism spectrum disorder. Pediatrics 2013; 132 (03) 445-453.
  • 12 Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health affairs 2011; 30 (03) 464-471.
  • 13 Carroll AE, Bauer NS, Dugan TM, Anand V, Saha C, Downs SM. Use of a Computerized Decision Aid for Developmental Surveillance and Screening: A Randomized Clinical Trial. JAMA Pediatr; 2014
  • 14 Carroll AE, Biondich P, Anand V, Dugan TM, Downs SM. A randomized controlled trial of screening for maternal depression with a clinical decision support system Journal of the American Medical Informatics Association. JAMIA; 2012
  • 15 Carroll AE, Biondich PG, Anand V, Dugan TM, Sheley ME, Xu SZ, Downs SM. Targeted screening for pediatric conditions with the CHICA system. Journal of the American Medical Informatics Association: JAMIA 2011; 18 (04) 485-490.
  • 16 Anand V, Carroll AE, Downs SM. Automated primary care screening in pediatric waiting rooms. Pediatrics 2012; 129 (05) e1275-1281.
  • 17 Downs SM, Zhu V, Anand V, Biondich PG, Carroll AE. The CHICA smoking cessation system. AMIA Annu Symp Proc. 2008: 166-170.
  • 18 Anand V, Biondich PG, Liu G, Rosenman M, Downs SM. Child Health Improvement through Computer Automation: the CHICA system. Stud Health Technol Inform 2004; 107 (01) 187-191.
  • 19 Downs SM, Biondich PG, Anand V, Zore M, Carroll AE. Using Arden Syntax and adaptive turnaround documents to evaluate clinical guidelines. AMIA Annu Symp Proc. 2006: 214-218.
  • 20 Downs SM, Carroll AE, Anand V, Biondich PG. Human and system errors, using adaptive turnaround documents to capture data in a busy practice. AMIA Annu Symp Proc. 2005: 211-215.
  • 21 Downs SM, Uner H. Expected value prioritization of prompts and reminders. Proceedings / AMIA Annual Symposium AMIA Symposium. 2002: 215-219.
  • 22 McDonald CJ, Overhage JM, Tierney WM, Dexter PR, Martin DK, Suico JG, Zafar A, Schadow G, Blevins L, Glazener T, Meeks-Johnson J, Lemmon L, Warvel J, Porterfield B, Cassidy P, Lindbergh D, Belsito A, Tucker M, Williams B, Wodniak C. The Regenstrief Medical Record System: a quarter century experience. International journal of medical informatics 1999; 54 (03) 225-253.
  • 23 Bauer NS, Sturm LA, Carroll AE, Downs SM. Computer decision support to improve autism screening and care in community pediatric clinics. Infants & Young Children 2013; 26 (04) 306-317.
  • 24 Robins DL, Casagrande K, Barton M, Chen CM, Dumont-Mathieu T, Fein D. Validation of the Modified Checklist for Autism in Toddlers, Revised With Follow-up (M-CHAT-R/F). Pediatrics 2014; 133 (01) 37-45.
  • 25 Robins DL, Dumont-Mathieu TM. Early screening for autism spectrum disorders: update on the modified checklist for autism in toddlers and other measures. J Dev Behav Pediatr 2006; 27 (02) S111-119.
  • 26 Chlebowski C, Robins DL, Barton ML, Fein D. Large-scale use of the modified checklist for autism in low-risk toddlers. Pediatrics 2013; 131 (04) e1121-1127.
  • 27 Stone WL. Cross-disciplinary perspectives on autism. J Pediatr Psychol 1987; 12 (04) 615-630.
  • 28 Steele AW, Eisert S, Davidson A, Sandison T, Lyons P, Garrett N, Gabow P, Ortiz E. Using computerized clinical decision support for latent tuberculosis infection screening. American journal of preventive medicine 2005; 28 (03) 281-284.
  • 29 Thomas KW, Dayton CS, Peterson MW. Evaluation of internet-based clinical decision support systems. Journal of medical Internet research 1999; 1 (02) E6.
  • 30 Nagykaldi Z, Mold JW. The role of health information technology in the translation of research into practice: an Oklahoma Physicians Resource/Research Network (OKPRN) study. J Am Board Fam Med 2007; 20 (02) 188-195.
  • 31 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA : the journal of the American Medical Association 1998; 280 (15) 1339-1346.
  • 32 Carroll AE, Bauer NS, Dugan TM, Anand V, Saha C, Downs SM. Use of a computerized decision aid for ADHD diagnosis: a randomized controlled trial. Pediatrics 2013; 132 (03) e623-629.
  • 33 Bauer NS, Carroll AE, Downs SM. Understanding the acceptability of a computer decision support system in pediatric primary care Journal of the American Medical Informatics Association. JAMIA; 2013
  • 34 Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association: JAMIA 2003; 10 (06) 523-530.
  • 35 Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, Tang PC. Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association: JAMIA 2001; 8 (06) 527-534.
  • 36 Parochka J, Paprockas K. A continuing medical education lecture and workshop, physician behavior, and barriers to change. J Contin Educ Health Prof. 2001; 21 (02) 110-116.
  • 37 Davis D, O’Brien MA, Freemantle N, Wolf FM, Mazmanian P, Taylor-Vaisey A. Impact of formal continuing medical education: do conferences, workshops, rounds, and other traditional continuing education activities change physician behavior or health care outcomes?. JAMA : the journal of the American Medical Association 1999; 282 (09) 867-874.
  • 38 Grimshaw JM, Shirran L, Thomas R, Mowatt G, Fraser C, Bero L, Grilli R, Harvey E, Oxman A, O’Brien MA. Changing provider behavior: an overview of systematic reviews of interventions. Med Care 2001; 39 (8 Suppl 2) II2-45.
  • 39 Oxman AD, Thomson MA, Davis DA, Haynes RB. No magic bullets: a systematic review of 102 trials of interventions to improve professional practice. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne 1995; 153 (10) 1423-1431.
  • 40 Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients’ care. Lancet 2003; 362 9391 1225-1230.
  • 41 Davis DA, Taylor-Vaisey A. Translating guidelines into practice. A systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne 1997; 157 (04) 408-416.
  • 42 Carbone PS, Behl DD, Azor V, Murphy NA. The medical home for children with autism spectrum disorders: parent and pediatrician perspectives. J Autism Dev Disord 2010; 40 (03) 317-324.
  • 43 Kosmicki JA, Sochat V, Duda M, Wall DP. Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Translational psychiatry 2015; 5: e514.
  • 44 Fusaro VA, Daniels J, Duda M, DeLuca TF, D’Angelo O, Tamburello J, Maniscalco J, Wall DP. The potential of accelerating early detection of autism through content analysis of YouTube videos. PLOS one 2014; 9 (04) e93533.
  • 45 Wall DP, Dally R, Luyster R, Jung JY, Deluca TF. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLOS one 2012; 7 (08) e43855.
  • 46 Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA. Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational psychiatry 2012; 2: e100.
  • 47 Duda M, Kosmicki JA, Wall DP. Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Translational psychiatry 2014; 4: e424.