Quantifying Downstream Healthcare Utilization in Studies of Genomic Testing

Zoë P Mackay; Dmitry Dukhovny; Kathryn A Phillips; Alan H Beggs; Robert C Green; Richard B Parad; Kurt D Christensen; BabySeq Project Team

doi:10.1016/j.jval.2020.01.017

Quantifying Downstream Healthcare Utilization in Studies of Genomic Testing

Value Health. 2020 May;23(5):559-565. doi: 10.1016/j.jval.2020.01.017. Epub 2020 Mar 20.

Authors

Zoë P Mackay¹, Dmitry Dukhovny², Kathryn A Phillips³, Alan H Beggs⁴, Robert C Green⁵, Richard B Parad⁶, Kurt D Christensen⁷; BabySeq Project Team

Affiliations

¹ Boston University School of Medicine, Boston, MA, USA.
² Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA.
³ Center for Translational and Policy Research on Personalized Medicine, Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA; Philip R Lee Institute for Health Policy, University of California San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA.
⁴ Harvard Medical School, Boston, MA, USA; Division of Genetics and Genomics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA, USA.
⁵ Harvard Medical School, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Partners Healthcare Personalized Medicine, Boston, MA, USA.
⁶ Harvard Medical School, Boston, MA, USA; Department of Pediatric Newborn Medicine, Brigham & Women's Hospital, Boston, MA, USA.
⁷ Harvard Medical School, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Precision Medicine Translational Research Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA. Electronic address: kurt_christensen@harvardpilgrim.org.

Abstract

Objectives: The challenges of understanding how interventions influence follow-up medical care are magnified during genomic testing because few patients have received it to date and because the scope of information it provides is complex and often unexpected. We tested a novel strategy for quantifying downstream healthcare utilization after genomic testing to more comprehensively and efficiently identify related services. We also evaluated the effectiveness of different methods for collecting these data.

Methods: We developed a risk-based approach for a trial of newborn genomic sequencing in which we defined primary conditions based on existing diagnoses and family histories of disease and defined secondary conditions based on unexpected findings. We then created patient-specific lists of services associated with managing primary and secondary conditions. Services were quantified based on medical record reviews, surveys, and telephone check-ins with parents.

Results: By focusing on services that genomic testing would most likely influence in the short-term, we reduced the number of services in our analyses by more than 90% compared with analyses of all observed services. We also identified the same services that were ordered in response to unexpected findings as were identified during expert review and by confirming whether recommendations were completed. Data also showed that quantifying healthcare utilization with surveys and telephone check-ins alone would have missed the majority of attributable services.

Conclusions: Our risk-based strategy provides an improved approach for assessing the short-term impact of genomic testing and other interventions on healthcare utilization while conforming as much as possible to existing best-practice recommendations.

Keywords: genetic testing; genomics; health services; healthcare utilization; humans; infant; medical records; newborn; risk factors; surveys and questionnaires; whole exome sequencing.

Publication types

Randomized Controlled Trial
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Female
Genetic Testing*
Genomics*
Humans
Infant
Longitudinal Studies
Male
Parents / psychology
Patient Acceptance of Health Care / statistics & numerical data*
Risk Factors
Surveys and Questionnaires
Telephone

Quantifying Downstream Healthcare Utilization in Studies of Genomic Testing

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Abstract

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