Health Informatics Assignment: EHR Adoption, AI, EMR Analysis

This assignment delves into the core of health informatics, examining the adoption rates of Electronic Health Records (EHRs) in the U.S., the historical challenges faced by early Artificial Intelligence (AI)-based systems in healthcare, and the current state of Electronic Medical Record (EMR) adop...Read More

Running head: - HEALTH INFORMATICS
HEALTH INFORMATICS
Name of the Student
Name of the University
Author Note

1HEALTH INFORMATICS
Table of Contents
Question-1..................................................................................................................................2
Question-2..................................................................................................................................3
Question-3..................................................................................................................................4
References..................................................................................................................................5

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Question-1
EHR is the foundation for clinical computing. What is the current adoption rate of
inpatient EHRs and ambulatory EHRs in the U.S.?
The adoption as well as the meaningful usage of Electronic Health Records (EHRs)
are the primary motives of the Health Information Technology for Economic and Clinical
Health (HITECH) act passed in the year of 2009 along with the Federal Health IT Strategic
Plan. Various data has been obtained from the American Hospital Association to put forward
a brief description regarding the trends within the process of adoption of the technology
related to EHR among the already existing non-federal acute caring hospitals from the year of
2008 to the year of 2015 (Rajkomar et al. 2018). This particularly keeps a track regarding the
adoption of the primary EHR systems along with the ownership of certified technology
belonging to EHR. The below figure puts forward all the data regarding the adoption of EHR
in the whole of U.S.
Figure-1: Adoption of EHRs
(Source- Dinev et al. 2016)

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The above figure puts forward the following information regarding the adoption rate
of inpatient EHRs as well as ambulatory EHRs in the whole of U.S as well as the following
information.
 Nearly all the hospitals that have been reported (96%) have the possession of a
certified technology belonging to EHR in the year of 2015 (Dinev et al. 2016).
 In the year of 2015, 84% of the total existing number of hospitals have specifically
adopted the primary EHR system that specifically represents an increase of nine fold
since the year of 2008.
 In the year of 2015, basic adoption of EHR had been visibly seen to have increased by
a count of 11% from that of 2014.
Question-2
Augmenting human cognition using machines’ computational power has been an
enduring topic in health informatics research, starting with the proliferation of artificial
intelligence (AI) based diagnostic systems developed from the 1960s to the 1980s. However,
few of these systems made their way to everyday clinical practice. What factors contributed
to the failure of the first generation of AI-based systems?
Artificial Intelligence has been most widely used for the maintenance of a proper
working for the newly developed EHR systems within the field of healthcare (Miotto, Kidd
and Dudley 2016). However, some of the factors that acted as a contributor towards the
creation of variable problems for Artificial intelligence that played a part in this regard are,
 It has been visibly identified that usage of artificial intelligence within the field of
healthcare will greatly reduce the direct interaction that doctors or the nurses carry out
with the patients.

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 Electronic Health Recorders (EHRs) with the addition of artificial intelligence are
blamed at every step for interfering into the patient-provider relationship.
 Digitalizing the entire EHR system with the help of artificial intelligence will put a
pause upon the human interaction that is still now the most preferred method to help
cure the patients off their illness.
Question-3
In Berner et al. (2005) “Will the Wave Finally Break? A Brief View of the Adoption of
Electronic Medical Records in the United States”, the authors showed great optimism
towards the widespread use of clinical computing in the next decade. You are required to
discuss the current state of EMR adoption with citation of at least two related work.
Less than a decade before, nine out of the ten doctors in the state of U.S. had been
updating the patient records manually and storing them in differently coloured files.
However, by the end of the year of 2017, an approximate count of 90% of the physicians
based in offices all around the nation had been using electronic health records (EHRs)
(Shickel et al. 2017). The rates of adoption are,
 In the month of March 2017, 67% among all the providers placed forward a report
regarding the usage of EHR that accounted to an increase of 1% from that of
September, in the year of 2016.
 Specialties belonging to physicians having the inclusion of highest rate for adoption
are paediatrics (76%), family practice (75%), nephrology (75%) and urology (74%).
 The states having the highest rate of adoption are Wyoming (79%), Utah (75%),
South Dakota (77%), Iowa (75%) and North Dakota (74%).

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References
Dinev, T., Albano, V., Xu, H., D’Atri, A. and Hart, P., 2016. Individuals’ attitudes towards
electronic health records: A privacy calculus perspective. In Advances in healthcare
informatics and analytics (pp. 19-50). Springer, Cham.
Miotto, R., Li, L., Kidd, B.A. and Dudley, J.T., 2016. Deep patient: an unsupervised
representation to predict the future of patients from the electronic health records. Scientific
reports, 6, p.26094.
Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus,
J., Sun, M. and Sundberg, P., 2018. Scalable and accurate deep learning with electronic
health records. NPJ Digital Medicine, 1(1), p.18.
Shickel, B., Tighe, P.J., Bihorac, A. and Rashidi, P., 2017. Deep EHR: a survey of recent
advances in deep learning techniques for electronic health record (EHR) analysis. IEEE
journal of biomedical and health informatics, 22(5), pp.1589-1604.