18 research outputs found
A study linking patient EHR data to external death data at Stanford Medicine
This manuscript explores linking real-world patient data with external death
data in the context of research Clinical Data Warehouses (r-CDWs). We
specifically present the linking of Electronic Health Records (EHR) data for
Stanford Health Care (SHC) patients and data from the Social Security
Administration (SSA) Limited Access Death Master File (LADMF) made available by
the US Department of Commerce's National Technical Information Service (NTIS).
The data analysis framework presented in this manuscript extends prior
approaches and is generalizable to linking any two cross-organizational
real-world patient data sources. Electronic Health Record (EHR) data and NTIS
LADMF are heavily used resources at other medical centers and we expect that
the methods and learnings presented here will be valuable to others. Our
findings suggest that strong linkages are incomplete and weak linkages are
noisy i.e., there is no good linkage rule that provides coverage and accuracy.
Furthermore, the best linkage rule for any two datasets is different from the
best linkage rule for two other datasets i.e., there is no generalization of
linkage rules. Finally, LADMF, a commonly used external death data resource for
r-CDWs, has a significant gap in death data making it necessary for r-CDWs to
seek out more than one external death data source. We anticipate that
presentation of multiple linkages will make it hard to present the linkage
outcome to the end user.
This manuscript is a resource in support of Stanford Medicine STARR (STAnford
medicine Research data Repository) r-CDWs. The data are stored and analyzed as
PHI in our HIPAA-compliant data center and are used under research and
development (R&D) activities of STARR IRB.Comment: 20 page
American Family Cohort, a data resource description
This manuscript is a research resource description and presents a large and
novel Electronic Health Records (EHR) data resource, American Family Cohort
(AFC). The AFC data is derived from Centers for Medicare and Medicaid Services
(CMS) certified American Board of Family Medicine (ABFM) PRIME registry. The
PRIME registry is the largest national Qualified Clinical Data Registry (QCDR)
for Primary Care. The data is converted to a popular common data model, the
Observational Health Data Sciences and Informatics (OHDSI) Observational
Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
The resource presents approximately 90 million encounters for 7.5 million
patients. All 100% of the patients present age, gender, and address
information, and 73% report race. Nealy 93% of patients have lab data in LOINC,
86% have medication data in RxNorm, 93% have diagnosis in SNOWMED and ICD, 81%
have procedures in HCPCS or CPT, and 61% have insurance information. The
richness, breadth, and diversity of this research accessible and research ready
data is expected to accelerate observational studies in many diverse areas. We
expect this resource to facilitate research in many years to come
Gradient Clogging in Depth Filtration
We investigate clogging in depth filtration, in which a dirty fluid is
``cleaned'' by the trapping of dirt particles within the pore space during flow
through a porous medium. This leads to a gradient percolation process which
exhibits a power law distribution for the density of trapped particles at
downstream distance x from the input. To achieve a non-pathological clogging
(percolation) threshold, the system length L should scale no faster than a
power of ln w, where w is the width. Non-trivial behavior for the permeability
arises only in this extreme anisotropic geometry.Comment: 4 pages, 3 figures, RevTe
Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information
<div><p>A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO<sub>2</sub>] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.</p></div
Exposure to radiation in daily life.
<p>Bar plot (upper panel: bars in blue) showing the amount of radiation that Participant #1 exposed to over a 25-d time window. Bar plot (lower panel: bars in magenta) showing the time that Participant #1 spent in airplane flights over the same time period. The maximum cruising altitude of each flight was labeled in the zoomed view of the bar plots. Asterisk represents the amount of radiation monitored during the airport carry-on luggage check (range 0.027 to 0.031 mRem). Other events that resulted in relatively high radiation are also labeled in the figure.</p
Circadian and diurnal patterns in physiological parameters.
<p>Participant #1 hourly summaries in (A) sleep, (B) HRs, (C) skin temperature, and (D) steps as measured using the Basis Peak device over 71 nontravel d. (E) Summaries of 43-person cohort for daily HR and skin temperature from all data and (F) differences in resting (fewer than five steps) nighttime and daytime HRs (Note: one person did not have nighttime measurements and is not included) and skin temperature. (G) Daily activity plots for 43 individuals. Based on number of peaks in the curves, four general patterns of activity behavior are evident. The plots in Fig 2G were aligned according to the first increase in activity.</p
Physiological and activity profiles for 43 individuals.
<p>(A) The relationship between the average number of steps per day and resting HR (<i>n</i> = 43) and (B) average steps per minute and change in body mass index (BMI; <i>n</i> = 20) over the course of approximately 1 y was analyzed. Average resting HRs (C) were calculated by gender (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001402#sec014" target="_blank">Material and Methods</a>; <i>n</i> = 38).</p