Sarah is interested in the design and evaluation of decision support systems for local and regional-scale climate adaptation. Her research aims to explore the social and cognitive processes through which decision support systems — both digital decision support tools and the activities of regional climate resilience networks — shape adaptation planning and implementation, organizational learning, and environmental outcomes. She is specifically interested in supporting more adaptive and integrated water resources management. Sarah’s current work focuses on better understanding the collaborative landscape of federal decision support activities using social network analysis, as well as the decision-making and planning processes of local stormwater managers in coastal communities across the U.S. using a mixed-methods approach, including surveys, interviews, and document analysis.

Sarah holds a BA in cognitive neuroscience from the University of Pennsylvania. Prior to coming to Stanford, she worked on health care innovation and equity research at the Penn Medicine Nudge Unit and the Center for Health Incentives and Behavioral Economics.

All Publications

  • I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes. Journal of the American Medical Informatics Association : JAMIA Apathy, N. C., Hare, A. J., Fendrich, S., Cross, D. A. 2022


    We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout.We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition.37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%).Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden.Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

    View details for DOI 10.1093/jamia/ocac211

    View details for PubMedID 36323282

  • Early Changes in Billing and Notes After Evaluation and Management Guideline Change ANNALS OF INTERNAL MEDICINE Apathy, N. C., Hare, A. J., Fendrich, S., Cross, D. A. 2022; 175 (4): 499-+


    The American Medical Association updated guidance in 2021 for frequently used billing codes for outpatient evaluation and management (E/M) visits. The intent was to account for provider time outside of face-to-face encounters and to reduce onerous documentation requirements.To analyze E/M visit use, documentation length, and time spent in the electronic health record (EHR) before and after the guideline change.Observational, retrospective, pre-post study.U.S.-based ambulatory practices using the Epic Systems EHR.303 547 advanced practice providers and physicians across 389 organizations.Data from September 2020 through April 2021 containing weekly provider-level E/M code and EHR use metadata were extracted from the Epic Signal database. We descriptively analyzed overall and specialty-specific changes in E/M visit use, note length, and time spent in the EHR before and after the new guidelines using provider-level paired t tests.Following the new guidelines, level 3 visits decreased by 2.41 percentage points (95% CI, -2.48 to -2.34 percentage points) to 38.5% of all E/M visits, a 5.9% relative decrease from fall 2020. Level 4 visits increased by 0.89 percentage points (CI, 0.82 to 0.96 percentage points) to 40.9% of E/M visits, a 2.2% relative increase. Level 5 visits (the highest acuity level) increased by 1.85 percentage points (CI, 1.81 to 1.89 percentage points) to 10.1% of E/M visits, a 22.6% relative increase. These changes varied by specialty. We found no meaningful changes in measures of note length or time spent in the EHR.The Epic ambulatory client base may underrepresent smaller and independent practices.Immediate changes in E/M coding contrast with null findings for changes in both note length and EHR time. Provider organizations are positioned to respond more rapidly to billing process changes than to changes in care delivery and associated EHR use behaviors. Fully realizing the intended benefits of this guideline change will require more time, facilitation, and scaling of best practices that more directly address EHR documentation practices and associated burden.None.

    View details for DOI 10.7326/M21-4402

    View details for Web of Science ID 000771992800001

    View details for PubMedID 35188791

  • Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity SCIENTIFIC REPORTS Fendrich, S. J., Balachandran, M., Patel, M. S. 2021; 11 (1): 21501


    Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

    View details for DOI 10.1038/s41598-021-01021-y

    View details for Web of Science ID 000714953500079

    View details for PubMedID 34728746

    View details for PubMedCentralID PMC8563736