Vital Times 2017 - 60

individual patient. However, to those of us that see our
patients one by one each day we can only see the trees
and not the forest.
The path to get from where we currently are, to a future
where we can actually leverage the EHR to help us
provide better care and actually improve outcomes most
likely lies in advances in computer science. As noted
above, current CDS algorithms are generally simplistic
and tend to rely on the presence or absence of one or
two items (e.g. medication allergy warnings taken from
allergy lists). This data may be imprecise and thus lead
to faulty warnings. Hopefully, future algorithms will pull
from dozens of different points, eventually even fillingin (imputing) the data that might be missing. These
algorithms will be far smarter and should be better at
helping us provide timely patient-centered care.
Hence, while work is underway in the journey towards
precision medicine, many hurdles remain. But with hard
work and time these obstacles will be knocked down
one by one. Just like someone using dial-up internet
in 1990s wouldn't imagine having handheld devices
streaming video at a whim, those of us using our EHRs
today cannot really envision what it will be like to care for
patients in 20 years.

Suggested Reading
Castaneda C1, Nalley K2, Mannion C3,
Bhattacharyya P3, Blake P2, Pecora A4, Goy A4,
Suh KS5. Clinical decision support systems for
improving diagnostic accuracy and achieving
precision medicine. J Clin Bioinforma. 2015 Mar
26;5:4.
Hsiao C-J, Hing E. Use and characteristics of
electronic health record systems among officebased physician practices: United States, 2001-
2013. NCHS data brief, no 143. Hyattsville, MD:
National Center for Health Statistics; 2014.
Charles D, King J, Patel V, Furukawa MF. "Adoption
of Electronic Health Record Systems among U.S.
Non-federal Acute Care Hospitals: 2008-2012",
ONC Data Brief, no 9. Washington, DC: Office of
the National Coordinator for Health Information
Technology; 2013.
JCGM 200:2008 International vocabulary of
metrology - Basic and general concepts and
associated terms (VIM)
Nair BG1, Gabel E, Hofer I, Schwid HA,
Cannesson M. Intraoperative Clinical Decision
Support for Anesthesia: A Narrative Review
of Available Systems. Anesth Analg. 2017
Feb;124(2):603-617.
Patrick J1, Li M. High accuracy information
extraction of medication information from
clinical notes: 2009 i2b2 medication extraction
challenge. J Am Med Inform Assoc. 2010 SepOct;17(5):524-7.
Etzioni DA1, Lessow C2, Bordeianou LG3,
Kunitake H3, Deery SE3, Carchman E4,
Papageorge CM4, Fuhrman G5, Seiler RL6,
Ogilvie J7, Habermann EB8, H Chang YH8,
Money SR9. Postoperative Myocardial Infarction in
Administrative Data vs Clinical Registry: A MultiInstitutional Study. J Am Coll Surg. 2017 Oct 10.
pii: S1072-7515(17)32000-8.
Gabel E, Hofer IS MD, Satou N, Grogan T, Shemin
R, Mahajan A MD PhD, Cannesson M. Creation
and Validation of an Automated Algorithm to
Determine Postoperative Ventilator Requirements
After Cardiac Surgery. Anesthesia & Analgesia
2017;124:1423-30.

60 | CSA Vital Times

Table of Contents for the Digital Edition of Vital Times 2017