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Module 1: Assignment(Healthcare data analytics course)


Examine the impact of data analytics on quality management


Summarize the following article:


· Your submission should be 1-3 pages – Title page and reference page do not count.  *Hint* you should have a title and reference page.

· Review all the topics and links in the

Article Summary Assignments

page in the Getting Started Module.

· The article and any supporting references should be APA-cited (see sample paper in Article Summary Assignments)

· You need to include the article’s main ideas and two specific examples.

Criteria for Success:

15 Points Possible

· 5 Points for completed submission of article summary.

· 5 Points (2.5 each) for citing specific examples from the article.

· 2.5 Points for properly citing in APA format.

· 2.5 Points for submission being free of major grammatical errors.


Article Summary Assignments

Use the following guidelines for writing and summarizing articles:

HOW TO WRITE ARTICLE SUMMARIES, REVIEWS & CRITIQUES(,unless%20they’re%20direct%20quotations.&text=Your%20summary%20should%20be%20about,length%20of%20the%20original%20article.)

When writing, be sure to consider the following:



Be organized. Use an outline. Plan before you write.



Write to your audience, which is an executive. Assume a formal tone and avoid using hyperbole or slang.



Write for impact and get to the point. Be concise with your diction. Use active verbs instead of passive verbs, e.g., say, ??The student writes the paper,? not ??The paper is written by the student.? Use a

minimalist approach(

· references used must be from industry or professional sources. Use the following APA citation guide:

Sample_APA_Paper__103017 copy-1.pdf


Page: 1


Posted on March 15, 2021 by Matthew

Category: Spring 2021

Page: 2

By Neelkamal Soares, MD; Sorabh Singhal; Casey Kloosterman, PharmD; and Teresa Bailey, PharmD.


Erroneous electronic health record (EHR) data capture is a barrier to preserving data integrity. We
assessed the impact of an interdisciplinary process in minimizing EHR data loss from prescription
orders. We implemented a three-step approach to reduce data loss due to missing medication

Step 1??A data analyst updated the request code to optimize data capture;
Step 2??A pharmacist and physician identified variations in EHR prescription workflows; and
Step 3??The clinician team determined daily doses for patients with multiple prescriptions in
the same encounter.

The initial report contained 1421 prescriptions, with 377 (26.5 percent) missing dosages. Missing
dosages reduced to 361 (26.3 percent) prescriptions following Step 1, and twenty-three (1.7 percent)
records after Step 2. After Step 3, 1210 prescriptions remained, including 16 (1.3 percent)
prescriptions missing doses. Prescription data is susceptible to missing values due to multiple data
capture workflows. Our approach minimized data loss, improving its validity in retrospective

Keywords: Data integrity, electronic health record, data abstraction, prescription.


By 2015 almost 96 percent of all non-federal acute-care hospitals possessed certified health
information technology (HIT), and 86 percent of office-based practices had adopted an electronic

health record (EHR).1,2 In addition to its primary function as a documentation tool used to store the
clinical narrative, the EHR is a rich source of large amounts of information, including pharmacy,
laboratory, financial, and patient demographic data. This gives the EHR utility for public health
practitioners and researchers.

This ??secondary use? of EHR data3 is especially powerful when combined with data from other

sources, e.g., census tracts, surveys, morbidity and mortality data.4

From the technical perspective, data quality in EHR-based studies remains a challenge for
investigators with respect to data completeness, standardization and the lack of use of controlled

vocabularies.3-6 The process can be time-intensive, as it involves sifting through raw EHR data that is
often disorganized and replete with uncodified variables. Furthermore, data point duplication errors
often require specialized personnel with knowledge of the EHR data structure to run meaningful

queries.7 Additionally, limitations include incomplete, inconsistent, or inaccurate data.5 Other barriers

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include the presence of multiple disconnected data capture systems, the lack of timely access to

EHR data, interoperability issues between EHR vendors,6-8 and changing data standards and practice

guidelines over time.9

We had experienced some of these limitations first-hand while evaluating prescribing patterns of
second-generation antipsychotic (SGA) medications in children and adolescents. Here we assess the
impact of an interdisciplinary, stepwise approach to improve the quality of data by reducing
missingness of data during an EHR data abstraction.


The Institutional Review Board of the primary author??s institution approved the study with exempt
status. We conducted a retrospective study using EHR data to evaluate SGA prescribing patterns in
children and adolescents between June 1, 2017, and December 31, 2018. This included data
collected from the Epic EHR (Epic Corp, Verona, WI). We utilized data from three health systems in
and around a small Midwestern city in the United States, encompassing a Federally-Qualified Health
Center, an academic teaching center, and a private community-based healthcare system. The
interdisciplinary team involved a physician, a pharmacist, a biostatistician, and a data analyst who
deidentified the data extracted from the EHR.

The requested data included all encounters during the specified time period for all patients under
the age of 18 at time of visit who received an SGA prescription at any time during the specified
period. The data analyst created a Structured Query Language (SQL) encounter-level report utilizing
an abstraction from Epic??s Clarity data warehouse. The Clarity database stores hospital-specific data,
which analysts query to create complex, data-intensive reports. The analyst masked the patient
medical record numbers (MRN) utilizing the community identification number (CID) within the Epic
software, which deidentified the data while allowing the research team to group hospital encounters
by individual patients. The query design utilized the medication list to pull medication names,
frequencies, and doses.

Upon discovering a significant number of encounters with missing medication doses, we were
unable to determine if the SGA encounter records without doses were different from the SGA
encounter records with doses; we therefore could not rule out the potential of a confounding
difference between the two subsets. This was our impetus to design a stepwise approach to
minimize missing SGA medication doses:

Step 1: The data analyst updated the data request code to optimize data capture. This was
necessary because the first query included all medication orders, including the cancelled and
discontinued orders. As researchers, we had incorrectly assumed these orders would not be added.
In order to be able to exclude these records from analysis, the analyst edited the query design
criteria to only include active and completed medication orders.

Page: 4

Step 2: the pharmacist and physician on the team reviewed the EHR prescription workflows to
identify and account for variations in prescribing methods.

Step 3: if there were multiple prescription records for the same patient on the same day, the clinician
team determined daily medication doses in one of three ways: (1) duplicative orders were deleted;
(2) multiple prescriptions with directions for different times of the day were added; or (3) records of
multiple SGA prescriptions were left on multiple rows.


A total of 346 individual patients met the inclusion criteria, representing 1,421 individual SGA
prescriptions. The initial query yielded 377 (26.5 percent) entries with missing data. In discussions
with the data analyst, we discovered ??abandoned? orders in the data set, where a clinician initiated
and then discarded a refill or new-prescription order. The data analyst thus required the database to
select against ??order status = cancelled?. This Step 1 intervention reduced the total SGA prescriptions
to 1374, with 361 missing dosages (26.3 percent).

Given the continued missing data, we implemented Step 2, with review of clinician orders using the
computerized physician order entry (CPOE) system. We found that clinicians were able to use ??free
text? fields to bypass the discrete dose data field. We then populated medication dose fields utilizing
the free-text instructions. The challenges encountered are listed in Table 1. This reduced the total
number of prescription orders with missing dosage values to 23 (1.7 percent). The remaining
unreconciled orders after Step 2 were ??blank? free-text fields without dose instructions.

The clinician team then further analyzed data for patients who received multiple SGA prescriptions
on the same day, and 373 prescription records met the criteria for Step 3 analysis. The data
represented 175 unique encounters (of which 34 encounters had two SGAs prescribed), that
contained prescriptions that were interpretable. Once the doses were interpretable, the final dataset
contained 1,210 prescriptions, and only 16 (1.3 percent) had a missing dose. The results are
summarized in Figure 1.


Despite utilizing standardized approaches to request specific data points to create an encounter-
level report, we discovered a high level of missing data that would have impacted the validity of our
intended analysis. Through a systematic, dialectic approach including all members of the team, we
initially addressed EHR data request issues of which we were unaware. Even after optimizing the
data request code, the persistent missing data required a ??manual? review via specific expertise of
investigators who were familiar with clinician prescribing workflows to reduce errors to an
acceptable, though non-zero, level. We particularly emphasize the amount of variability in
prescription ordering workflows within the sample; clinicians often tailor established EHR workflows

to their preferences and personalize their approaches to using EHR features.10 Due to this

Page: 5

widespread practice, it is critical to recognize and capture this variation when evaluating data sets
for accuracy and validity.

Medication prescribing orders are particularly vulnerable to errors related to the common structure
of these orders. CPOE medication fields consist of three main regions: the prescription or ??Rx line?,
which contains medication name, strength amount and units, and drug form; the patient instructions
or ??Sig line?, which includes dose units, route of administration, frequency and duration in days; and a
??Special Instructions line?, which includes detailed instructions and clarifications. It is the latter that

generates the free-text field.11 In the clinical setting, prescriptions are used essentially as a
communication tool between clinicians and pharmacists. Thus, discrepant or unclear instructions are
resolved using either the pharmacists?? knowledge or follow-up communication between the two

providers, a system that leads to inefficiencies.11

We found variable free-text data that led to analysis discrepancy after the first intervention in up to

24.7 percent of the data points, which was less than Palchuk??s study,11 but still required Step 2 of the
intervention. Multiple strategies have been proposed to draw meaningful information from indiscrete

data, including using established databases and rule-based systems12 and machine learning

analyses.13 Instead of retrospective approaches for data extraction and ??cleaning?, we stress the
importance of proactive approaches that educate clinicians on the importance of following standard
CPOE prescription workflows in the EHR. One strategy is to optimize prescribing workflows through
a user-centric CPOE system, which includes using drop-down menus for drug dosing options. A
system that prioritizes selecting commonly found medication doses and routes could prevent

clinicians from bypassing discrete entry in favor of free-text options.14 Another way is to implement

clinical decision support (CDS) processes that can perform checks on free-text orders11 though there

are limits to both the deployment and functioning of CDS.15 Until such time that a singular or
combination of strategies are used to automate mitigation of errors in prescribing input, we believe
that research teams should utilize an interdisciplinary approach with content-expert team members
to contribute to the process. As evident from our method, although data analysts are integral to the

data pull and serve as the ??honest broker? for research protocols,16 they are often unable to account
for the nuances in clinical workflows that impact data quality. This underscores the importance of
having a clinician on the investigator team who is intimately familiar with the clinical process where
the data are generated and collected. Similarly, clinical investigators often have limited knowledge
of how data is structured within the EHR, and the analyst is needed to implement changes, as was
the case in our Step 1 with the cancelled orders. An interdisciplinary approach has grown in

healthcare research17 and has particular value in EHR-based research involving informaticians and
clinicians. However, not every institution has access to a clinical informatician, hence it is critical to

Page: 6

build teams with interdisciplinary content expertise.

The limitations of our study included data from a single EHR covering three organizations in a single
geographic area, and hence results may not be generalizable to other regions or EHRs. However,
replication with larger sets of data points and collating data from multiple sources can extend our
model and test its value. This was also a cross sectional approach, looking at a defined set of
prescriptions as part of larger project. It would be important to look how pervasive the errors are
when expanded over a longer period of time. Furthermore, system workflows change over time, so

any follow-up studies must account for practice or guidelines changes.18 Finally, we did not compare
a human review to machine learning or other automated methods.


An interdisciplinary, stepwise approach to reducing EHR-based prescription data loss can be
effective. Prescription data is particularly susceptible to missing values given the multiple methods
of data entry, including the use of free-text fields, sometimes replete with medical abbreviations and
typographical errors. We advocate that EHR developers explore ways to simplify interfaces and
options with an eye to reduce human error opportunities, so that clinicians can document in the EHR
using the method that best matches their workflow. Multiple methods of data entry can complicate
the secondary use of EHR-data for research. On research teams involving EHR data, input from
clinicians and other content specialists is critical to optimizing the completeness, accuracy and
validity of an EHR-data abstraction, since quality of studies that use EHR data are only as good as
the data that is available.

Disclosures and Acknowledgements

The authors have no conflicts of interest to disclose.

The authors attest that we have not submitted this manuscript for publication elsewhere.

The authors would like to acknowledge Dr. Phillip Kroth for his input and guidance, Joseph Billian for
statistical support, and Melissa Sherfield and Melissa Medina for their database and analytical

Author Biographies

Neelkamal Soares, MD (

[email protected]

) is Professor of Pediatric & Adolescent
Medicine, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, Michigan.

Sorabh Singhal (

[email protected]

) is a Medical Student, Western Michigan University
Homer Stryker M.D. School of Medicine, Kalamazoo, Michigan.

Casey Kloosterman, PharmD, (

[email protected]

) is a doctoral graduate, Ferris State University
College of Pharmacy, Big Rapids, Michigan.


[email protected]


[email protected]


[email protected]

Page: 7

Teresa Bailey, PharmD, (

[email protected]

) is Professor of Pharmacy Practice, Ferris State
University College of Pharmacy, Big Rapids, Michigan.


1. Adler-Milstein J, Holmgren AJ, Kralovec P, et al. ??Electronic Health Record Adoption in US
Hospitals: The Emergence of a Digital ??Advanced Use?? Divide.? J Am Med Inform Assoc 2017; 24 (6):

2. The Office of the National Coordinator for Health Information Technology. ??Health IT Dashboard.?
Accessed March 2020.

3. Safran C, Bloomrosen M, Hammond WE, et al. ??Toward a National Framework for the Secondary
Use of Health Data: An American Medical Inform atics Association White Paper. J Am Med Inform
Assoc 2007. 14 (1): 1??9.

4. Casey JA, Schwartz BS, Steward WF, et al. ??Using Electronic Health Records for Population Health
Research: A Review of Methods and Applications.? Annu Rev Public Health 2016. 37 (1): 61??81.

5. Botsis T, Hartvigsen G, Chen F, et al. ??Secondary Use of EHR: Data Quality Issues and Informatics
Opportunities.? Summit Transl Bioinform 2010: 1??5.

6. Cowie MR, Blomster JJ, Curtis LH et al. ??Electronic Health Records to Facilitate Clinical Research.?
Clin Res Cardiol 2017. 106 (1): 1??9.

7. Milinovich A, Kattan MW. ??Extracting and Utilizing Electronic Health Data from Epic for Research.?
Ann Transl Med 2018. 6 (3): 42??42.

8. Goldstein BA, Navar AM, Pencina MJ, et al. ??Opportunities and Challenges in Developing Risk
Prediction Models with Electronic Health Records Data: A Systematic Review.? J Am Med Inform
Assoc 2017. 24 (1): 198??208.

9. Evans RS. ??Electronic Health Records: Then, Now, and in the Future.? Yearb Med Inform 2016. 25 (S
01): S48??61.

10. Ancker JS, Kern LM, Edwards A, et al. ??How Is the Electronic Health Record Being Used? Use of
EHR Data to Assess Physician-Level Variability in Technology Use.? J Am Med Inform Assoc 2014. 21
(6): 1001??8.

11. Palchuk MB, Fang EA, Cygielnik JM, et al. ??An Unintended Consequence of Electronic
Prescriptions: Prevalence and Impact of Internal Discrepancies.? J Am Med Inform Assoc 2010. 17 (4):

12. Karystianis G, Sheppard T, Dixon WG, et al. ??Modelling and Extraction of Variability in Free-Text
Medication Prescriptions from an Anonymised Primary Care Electronic Medical Record Research
Database.? BMC Med Informs Decis Mak 2015. 16 (1): 18.


[email protected]

Page: 8

13. Tao C, Filannino M, Uzuner Ã?. ??Prescription Extraction Using CRFs and Word Embeddings.? J
Biomed Inform 2017. 72 (August): 60??66.

14. Brown CL, Mulcaster HL, Triffitt KL, et al. ??A Systematic Review of the Types and Causes of
Prescribing Errors Generated from Using Computerized Provider Order Entry Systems in Primary and
Secondary Care.? J Am Med Inform Assoc 2017. 24 (2): 432-440.

15. Wright A, Hickman TT, McEvoy D, et al. ??Analysis of Clinical Decision Support System
Malfunctions: A Case Series and Survey.? J Am Med Inform Assoc 2016. 23 (6): 1068??76.

16. Rajiv D, Patel AA, Winters S, et al. ??A Multidisciplinary Approach to Honest Broker Services for
Tissue Banks and Clinical Data.? Cancer 2008. 113 (7): 1705??15.

17. Van Noorden R. ??Interdisciplinary Research by the Numbers.? Nature 2015. 525 (7569): 306??7.

18. Agniel D, Kohane IS, Weber GM. ??Biases in Electronic Health Record Data Due to Processes within
the Healthcare System: Retrospective Observational Study.? BMJ 2018. 363: k4416.

Reproduced with permission of copyright owner. Further reproduction
prohibited without permission.


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