0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Research Letter |

Use of Electronic Health Records for Automated Screening of Growth Disorders in Primary Care FREE

Ulla Sankilampi, MD, PhD1; Antti Saari, MD2; Tiina Laine, MD, PhD3; Päivi J. Miettinen, MD, PhD3; Leo Dunkel, MD, PhD4
[+] Author Affiliations
1Department of Pediatrics, Kuopio University Hospital, Kuopio, Finland
2Department of Pediatrics, University of Eastern Finland, Kuopio
3Helsinki University Central Hospital, Helsinki, Finland
4Centre for Endocrinology, Queen Mary University of London, London, England
JAMA. 2013;310(10):1071-1072. doi:10.1001/jama.2013.218793.
Text Size: A A A
Published online

Monitoring of linear growth is a well-established part of pediatric health care in the developed world. Although monitoring aims to support early diagnosis and timely treatment of disorders affecting growth, such disorders are often diagnosed late.1 This population-based study assessed the effectiveness of a novel computerized and automated growth monitoring (AGM) strategy integrated into an electronic health record (EHR) system in the primary care setting. Our hypothesis was that compared with standard growth monitoring (SGM), the AGM strategy would result in a better diagnostic yield and facilitate earlier diagnosis of disorders affecting growth.

Approximately 98% of the child population in Finland participates in a growth monitoring program, including 20 height measurements from the first postbirth measurement to aged 12 years.2 At scheduled visits, trained primary care nurses measure supine length (up to 24 months) or height using standardized techniques and equipment.2 The nurses analyze the length and height measures using 3 screening algorithms: (1) against population-based length and height references; (2) for distance from target height (calculated from parental heights); and (3) for change in growth rate (for details, see3).

In this study, a prospective 1-year (2008-2009) AGM intervention was performed in the primary care setting of 1 municipality in Finland. The preceding 3 years (2005-2008) were used as a comparator (SGM). Automated growth monitoring included 2 additional automated steps in addition to SGM. First, the longitudinal growth data set of each measured child was automatically analyzed by the EHR integrated screening algorithms. Second, abnormal growth values were automatically transferred to a pediatric endocrinologist for review of the growth data. There were obvious erroneous height values in 3.1% of cases that were requested to be corrected. The endocrinologist then provided electronic consultation to the primary care physician in regard to further actions, but referral to secondary care was still dependent on the judgment of the primary care physician. The primary outcome was the diagnostic yield of primary or secondary growth disorders during the AGM intervention year vs the SGM control years.4

Ethical approval was given by the local institutional ethics review board without the requirement for informed consent. A 2-sided threshold of P< .05 (χ2 and Fisher exact tests) was used to evaluate statistical significance; SPSS version 19.0 was used for all statistical analyses (SPSS Inc).

During the control years, an annual mean (SD) of 33 029 (273) children were screened. A mean (SD) of 4 (1) children were diagnosed with a new primary or secondary growth disorder. During the AGM intervention year, the number of new diagnoses was 28 among the 32 404 screened children (88% were measured in the previous SGM years; Figure). The diagnostic yield of primary or secondary growth disorders was 0.1 (95% CI, 0-0.3) per 1000 screened children in the control years vs 0.9 (0.6-1.2) per 1000 in the AGM year (P< .001) (Table).

Place holder to copy figure label and caption
Figure.
Clinical Effectiveness of Automated Growth Monitoring Integrated in an Electronic Health Record in Comparison to Standard Growth Monitoring

aData have been rounded. bDefined as the height of the individual more than 2 SD below the corresponding mean height for a given age and sex and in whom no identifiable disorder is present. cAccording to classification by the European Society for Pediatric Endocrinology.4

Graphic Jump Location
Table Graphic Jump LocationTable.  Individual Diagnoses of Primary and Secondary Growth Disorders

Referral to specialist care for a suspected growth disorder increased from an annual mean (SD) of 72.7 (5.0) referrals during the control years (0.22% [95% CI, 0.18%-0.28%] of measured children) to 209 (0.64%; 95% CI, 0.56%-0.74%) during the AGM year (P< .001; Figure).

Fourteen of the 28 children (50.0%; 95% CI, 32.6%-67.4%) diagnosed during the AGM year had 1 or more abnormal height measurements before the AGM intervention, with a median delay in diagnosis of 1.79 years (range, 0.08-10.26 years; Table).

In this population-based cohort study, we showed that screening of growth disorders using algorithms integrated into an EHR system was associated with a higher rate of detection and referral to specialist care. Balancing the increased diagnostic yield is the increased workload of specialists and costs. We identified prevalent cases who were missed by the SGM in preceding years, which may partly explain the exceptionally good results of the 1-year AGM intervention. The subsequent detection rate of growth disorders with AGM remains to be established, but is likely to be lower. Because SGM was in use throughout the country, a more informative type of study design than the pre-post comparison was not possible. Whether the results are generalizable to other countries remains to be determined.

Section Editor: Jody W. Zylke, MD, Senior Editor.

Corresponding Author: Leo Dunkel, MD, PhD, Centre for Endocrinology, William Harvey Research Institute, Queen Mary University of London, Barts and the London Charterhouse Square, London, EC1 M 6BQ, England (l.dunkel@qmul.ac.uk).

Author Contributions: Dr Dunkel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Sankilampi, Saari, Dunkel.

Acquisition of data: Sankilampi, Saari, Dunkel.

Analysis and interpretation of data: Sankilampi, Saari, Laine, Dunkel.

Drafting of the manuscript: Sankilampi, Saari, Laine, Miettinen, Dunkel.

Critical revision of the manuscript for important intellectual content: Sankilampi, Saari, Dunkel.

Statistical analysis: Sankilampi.

Obtained funding: Sankilampi, Dunkel.

Administrative, technical, or material support: Sankilampi, Dunkel.

Study supervision: Dunkel.

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Sankilampi reported receiving travel support from Merck Serono. No other disclosures were reported.

Funding/Support: This study was supported by the Finnish Funding Agency for Technology and Innovation and Kuopio University Hospital.

Role of the Sponsor: The Finnish Funding Agency for Technology and Innovation and Kuopio University Hospital had no role in the 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.

Additional Contributions: We thank Laura Valpio, MD (University of Eastern Finland, Kuopio), for her help with data acquisition. Dr Valpio did not receive compensation for her contribution.

Craig  D, Fayter  D, Stirk  L, Crott  R.  Growth monitoring for short stature: update of a systematic review and economic model. Health Technol Assess. 2011;15(11):iii-iv, 1-64.
PubMed
Mäki  P, Wikström  K, Hakulinen-Viitanen  T, Laatikainen  T, eds. Health Checkups at Well-Child Clinics: Methodological Handbook. Tampere, Finland: Juvenes Print; 2011.
Sorva  R, Tolppanen  EM, Lankinen  S, Perheentupa  J.  Growth evaluation: parent and child specific height standards. Arch Dis Child. 1989;64(10):1483-1487.
PubMed   |  Link to Article
Wit  JM, Ranke  MB, Kelnar  CJH.  ESPE classification of paediatric endocrine diagnoses. Horm Res. 2007;68(suppl 2):1-120.
Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Clinical Effectiveness of Automated Growth Monitoring Integrated in an Electronic Health Record in Comparison to Standard Growth Monitoring

aData have been rounded. bDefined as the height of the individual more than 2 SD below the corresponding mean height for a given age and sex and in whom no identifiable disorder is present. cAccording to classification by the European Society for Pediatric Endocrinology.4

Graphic Jump Location

Tables

Table Graphic Jump LocationTable.  Individual Diagnoses of Primary and Secondary Growth Disorders

References

Craig  D, Fayter  D, Stirk  L, Crott  R.  Growth monitoring for short stature: update of a systematic review and economic model. Health Technol Assess. 2011;15(11):iii-iv, 1-64.
PubMed
Mäki  P, Wikström  K, Hakulinen-Viitanen  T, Laatikainen  T, eds. Health Checkups at Well-Child Clinics: Methodological Handbook. Tampere, Finland: Juvenes Print; 2011.
Sorva  R, Tolppanen  EM, Lankinen  S, Perheentupa  J.  Growth evaluation: parent and child specific height standards. Arch Dis Child. 1989;64(10):1483-1487.
PubMed   |  Link to Article
Wit  JM, Ranke  MB, Kelnar  CJH.  ESPE classification of paediatric endocrine diagnoses. Horm Res. 2007;68(suppl 2):1-120.
Link to Article

Letters

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles