JMIR MEDICAL INFORMATICS

Zhan et al

Original Paper

Application of Failure Mode and Effects Analysis to Improve theQuality of the Front Page of Electronic Medical Records in China:
Cross-Sectional Data Mapping Analysis
Siyi Zhan*, MMed; Liping Ding*, BSc; Hui Li, MMed; Aonan Su, MMed
Zhejiang Provincial People's Hospital, Hangzhou, China*

these authors contributed equally

Corresponding Author:
Aonan Su, MMed
Zhejiang Provincial People's Hospital
No. 158, Shangtang Rd
Hangzhou, 310000
China
Phone: 86 18814885258
Email: suaonan_512917@126.com

Abstract
Background: The completeness and accuracy of the front pages of electronic medical records (EMRs) are crucial for evaluating
hospital performance and for health insurance payments to inpatients. However, the quality of the first page of EMRs in China's
medical system is not satisfactory, which can be partly attributed to deficiencies in the EMR system. Failure mode and effects
analysis (FMEA) is a proactive risk management tool that can be used to investigate the potential failure modes in an EMR system
and analyze the possible consequences.
Objective: The purpose of this study was to preemptively identify the potential failures of the EMR system in China and theircauses and effects in order to prevent such failures from recurring. Further, we aimed to implement corresponding improvements
to minimize system failure modes.
Methods: From January 1, 2020, to May 31, 2022, 10 experts, including clinicians, engineers, administrators, and medical
record coders, in Zhejiang People’s Hospital conducted FMEA to improve the quality of the front page of the EMR. The
completeness and accuracy of the front page and the risk priority numbers were compared before and after the implementationof specific improvement measures.
Results: We identified 2 main processes and 6 subprocesses for improving the EMR system. We found that there were 13
potential failure modes, including data messaging errors, data completion errors, incomplete quality control, and coding errors.
A questionnaire survey administered to random physicians and coders showed 7 major causes for these failure modes. Therefore,
we established quality control rules for medical records and embedded them in the system. We also integrated the medical
insurance system and the front page of the EMR on the same interface and established a set of intelligent front pages in the EMR
management system. Further, we revamped the quality management systems such as communicating with physicians regularly
and conducting special training seminars. The overall accuracy and integrity rate of the front page (P<.001) of the EMR increased
significantly after implementation of the improvement measures, while the risk priority number decreased.
Conclusions: In this study, we were able to identify the potential failure modes in the front page of the EMR system by using
the FMEA method and implement corresponding improvement measures in order to minimize recurring errors in the health careservices in China.
(JMIR Med Inform 2024;12:e53002) doi: 10.2196/53002
KEYWORDS
front page; EMR system; electronic medical record; failure mode and effects analysis; FMEA; measures

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Introduction
The electronic medical record (EMR) system is the main carrierof medical information that has details about the whole processof a physician’s treatment for a patient [1]. The information on
the front page of the EMR is condensed, which includes apatient’s basic information, disease diagnosis, information on
surgical or invasive operations, and medical expenses [2]. Since
January 1, 2013, almost all tertiary hospitals in China havesubmitted the front pages of the EMRs of inpatients to the
Hospital Quality Monitoring System led by the Bureau of
Medical Administration and Medical Service Supervision andNational Health and Family Planning Commission of thePeople’s Republic of China [3]. The quality and managementof the front pages of EMRs are critical for their application in
medical services [4], research [2,5], education [6], and hospitalmanagement [7]. For example, some indicators for assessing
the capacity of hospital medical services, such as the services
for surgery and disease diagnosis, often utilize the information
through the front page of the EMR for statistical purposes.
However, there are many difficulties in the management of thefront page of EMR. A survey conducted by the National Medical
Record Management Quality Control Center of China [8]showed that more than 230 million front pages of EMRs in 2020are established in China. Each of them contain over 100 fields.
However, there are only 2.5 full-time coders on average in eachhospital among 5439 medical institutions, and only 67.9% ofthem perform special quality control, while 24.2% of them use
information technology to control the quality of the front pageof the EMR system.
For reforming the medical insurance payment methods in China,
the Chinese State Council’s version of health insurance issued
a notice in 2019 on the issuance of technical specifications andgrouping schemes for the national pilot of diagnosis-relatedgrouping payments for diseases [9]. Therefore, the front pageof an EMR needs to be uploaded on the websites of the Healthand Wellness Committee and the Health Insurance Authority,
which means coders need to edit a front page twice to meet thedifferent needs of both the sectors. The former is for hospital
performance evaluation and the latter is for patient healthinsurance payment. The introduction of this policy in 2019
increased the difficulty of medical record management.
Failure mode and effects analysis (FMEA) is a proactive risk
management tool that originated in the US military in the 1940s.
It is widely applicable to human, equipment, and system failuremodes, as well as hardware and software programs. FMEA finds
out all the potential failure modes in a system and analyzes their
possible consequences by mapping the subsystems and each
subprocess that makes up the process one by one in the productdesign stage and process design stage [10]. Thus, the advantage
of FMEA is that problems can be identified and improved during
the system development phase to avoid possible problems.
Moreover, the costs incurred to address software defects andfailures at an early stage are lower compared to those incurredto address defects at a later stage. Initially, FMEA was widelyused in engineering [11], food safety management [12], financial
management [13], and so on. Thereafter, with the rising demands
in health care services, FMEA was used for proactive healthhttps://medinform.jmir.org/2024/1/e53002

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Zhan et alcare risk analysis. Doctors often use the EMR system to record
patients’ visits. Any issue in the EMR system can affect thepatient’s visit process and visit records. According to asystematic review [14], 158 studies published from 1998 to2018 and classified under 4 categories, namely, health care
process, hospital management, hospital informatization, andmedical equipment and production, reported the use of FMEA
in health care systems for proactive health care risk evaluation.
In FMEA, the risk priority number (RPN) is calculated by giving
a numerical value (scoring) for the severity, frequency, anddetectability of the risks or failures, which enable risk
assessment of the system [10]. An EMR system named Heren(Zhejiang Heren Technology Corporation), which is installedin many hospitals in China, is used by physicians and medical
record management coders and quality controllers for fillingout the front page. The purpose of this study was to identify thepossible failures in the front page data of the EMR and theircauses and effects and to propose specific improvement
measures to minimize errors. Moreover, we aimed to compare
the EMRs before and after introducing the measures to verify
the efficacy of the improvement measures. For this, we reviewed
previous relevant literature through PubMed, Embase, Web of
Science, and Cochrane Library. During this review, we foundthat although FMEA has been used in some studies for
improvement of some facets of EMRs, no study has used FMEA
for improving the efficiency the front page of the EMR [15,16].
Thus, to the best of our knowledge, ours is the first study toapply FMEA to identify the potential failures on the front pageof the EMR in China and the causes and effects of these failures
and to perform a before-and-after comparison of the revised
front page of the EMR.

Methods
Study Design
We conducted a cross-sectional study from January 1, 2020, to
May 31, 2022, in Zhejiang People’s Hospital, which is one of
the largest public hospitals in Zhejiang province with more than
100,000 hospital discharges per year. During the period of ourresearch, the number of hospital discharges reached 250,774,
which means the same number of front pages of EMRs needed
to be filled and coded.

Steps of FMEA
Assembling a Panel for FMEA
Ten experts, including clinicians, medical record coders, andhospital administrators, were invited to assess the potential risksof the EMR system in China. Since coders and quality
controllers were necessary to ensure the accuracy of the front
page of the EMR, only those who had been working full-timeon this task for more than 5 years and who had achieved acoding accuracy rate of more than 95% and who had checked
more than thousands of medical records for quality were
included. Before we began our study, the organizer introduced
the theme of our study to ensure that every expert knew theprocess of FMEA and the importance of a front page of an EMR.
Then, the time and place for each discussion was planned toensure that the process ran smoothly.
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Mapping the Process and Subprocesses
Each expert mapped the process and subprocess of completinga front page of an EMR alone initially to avoid interference
from others. For example, there are 2 data sources for the content
on the front page of the EMR: information automaticallyimported from the hospital information system that is mainly
used by physicians and information that is filled in manuallyby the physician. Thus, different experts could map their own
process according to their work experience. Thereafter, allexperts were gathered to draw the final process and subprocess
to achieve the completeness of the whole system.

Brainstorming to Identify Potential Failure Modes in
Each Subprocess and Their Causes and Effects
The implementation process of this step is consistent with themapping process. At first, each expert could think about every
potential failure mode individually. Then, all the experts
summarized all the modes and discussed many more potential
failure modes by brainstorming once again. In addition, theviews on effects and reasons for failure modes were exchanged
by experts. Since there were so many issues that could result in
potential failure modes, our team summarized the main causes
and created a questionnaire for randomly selected physicians
and coders to answer.

Zhan et al
Our team used the Pareto principle to identify the pressing
causes that need to be addressed. Then, the experts proposed
one or more corresponding improvement measures for eachfailure mode. Further, the feasibility and effectiveness ofimprovement measures were also discussed.

Comparing the Quality Before and After the
Improvements
The experts evaluated the quality of the front page of the EMR
before and after the application of the improvement measures.
The RPN score was bound to improve if these improvement
measures were effective.

Ethical Considerations
This study did not involve any patient data or ethical data, andthe ethics approval committee of Zhejiang Provincial People's
Hospital specified that no ethics approval was required.

Statistical Analysis
We performed statistical analyses using SPSS (version 20.0;
IBM Corp). Two-sided t tests were performed to compare the
RPNs of the front page of the EMR before and after applying
the improvement measures. P values <.05 were considered
statistically significant.

Calculating the RPN

Results

A scoring criterion was used to evaluate the severity, frequency,
and detectability of the failures, and each dimension was divided
into 10 points. Then, the RPN was calculated by using the scoreof the 3 dimensions (RPN = Severity × Frequency ×
Detectability) to evaluate the final score of each failure mode,
which ranges from 0 to 1000. To improve the consistency andaccuracy of scoring, the rating weight of each expert was basedon their professional title grade, work experience, and familiarity
with FMEA. In addition, a risk assessment criterion wasestablished to avoid any dispute about the scores given by theexperts.

Assembling a Panel for FMEA

Proposing Improvement Measures for Each Failure
Mode
Since a low RPN could result from severity, frequency, or
detectability and a low score for each dimension could be caused
by many different reasons, it is necessary to find out the main
issues. According to the Pareto principle, 80% of theconsequences are due to only 20% of the potential causes [17].

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Our expert panel consisted of 10 experts in 5 different fields.
There were 4 physicians, 2 coders, 2 hospital administrators, 1
quality control staff, and 1 information engineer who expressed
different views and opinions on the front page of the EMR.

Mapping the Process and Subprocesses of the Front
Page of EMR
The expert panel identified 2 main process steps and 6
subprocesses after discussion (Figure 1). The 2 main process
steps were management of the physician’s system andmanagement of the medical record system. The 6 subprocesses
were information import from physicians’ hospital information
system, front page filled by physicians, front page quality control
by physicians, transmission of information in the EMR system,
coders’ proofreading and coding, and front page quality control
by EMR management.

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Figure 1. Process map of the front page of the electronic medical record system.

Brainstorming to Identify Potential Failure Modes in
Each Subprocess and Their Causes and Effects
The front page of the EMR that was evaluated in this study isshown in Figure 2. According to the process map of the front
page of the EMR, the expert panel found that there were 13
potential failure modes, which can be mainly divided into 2
categories. One category is the low accuracy in a variety of
information, including basic patient information, treatment

information, and cost information. The other category is the
low detection in a variety of information, including incorrectcase header coding, incomplete quality control, and transmission
errors. Regarding the causes for the failures, 115 physicians andcoders filled out a questionnaire and summarized 15 maincauses. The main causes were incompleteness of the front page,error in information, or incorrect diagnosis-related group, which
is risky for hospital medical quality management, academic
research, and medical insurance payment (Table 1).

Figure 2. Front page of the electronic medical record.

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Table 1. Potential failure modes with their causes and effects.
Process

Failure modes

Transmission in the hospital •information system••

Front page filled by physicians••

Front page quality control
by physicians••Reasons

Basic information transmission error •Inpatient information transmission error
Expenses information transmission
error

Data interface errors

Incorrectly filled-in medical informa- •tion
Incorrectly filled in other information ••Do not understand the filling
criteria
Do not fill in carefully
Incomplete quality control reminders

•

•
•

•

•

No emphasis on quality control
Unfamiliar with quality control
rules
Complexity of quality control
rules
Lack of information assistance

Inconsistency between the received
data in the EMR system and original
data

•
•

Data interface errors
Encoding conversion error•

No data errors were foundWrong code for diagnosis, surgery, or
operation
Restoration error
Diagnostic and surgical operation
codes do not meet the requirements ofpatients’ insurance•

Formal quality control rules are •too simple•
Lack of internal quality control
remindersInsufficient professional capacity of coders
Few training opportunities forcodersInadequate communication between coders and doctors
The criteria are different between the requirements of patients’ insurance and front page

Erroneous data persist
The DRG is wrong

Using a sampling model to con- •duct quality control
Insufficient professional capaci- •ty of quality control staff•
Complexity of quality control
rules
Lack of information assistanceUnable to find all errors on
the front page
Erroneous data persist
The DRG is wrong

Inadequate quality control
Inaccurate quality control•

Transmission in the physi-•

cians’ EMRb system

Coders’ proofreading andcoding

•
•
•
•

•
•
•
•
•

Front page quality control
by EMR management••

Inadequate quality control•
Late feedback for the results of quality
control•••aEffects•

The original data on the front
page are erroneous•

The DRGa is erroneous
Affects patients’ medical reimbursement

•

•
•

•
•

•

The original data on the front
page are erroneous
The DRG is erroneous
Affects patients’ medical reimbursement
The original data on the front
page are erroneous
The DRG is wrong
Affects patients’ medical reimbursement

The original data on the front
page are wrong
The DRG is wrong

DRG: diagnosis-related group.

b

EMR: electronic medical record.

Calculating the RPN
Before calculating the RPN, a risk assessment criterion wasestablished to evaluate the quality of the front page of the EMR(Table 2).

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The rating weight of each expert was calculated to reduce theinfluence caused by the individual subjective factors of theexperts (Table 3).

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Table 2. Risk assessment criteria for the quality management of the front page of the electronic medical record system.
Grade

Severity

Criteria for risk severity

Frequency

Criteria for risk frequency

Detectability

Criteria for risk detectability

10

Very highMake the score of the front

Extremely
highEvery time

Very low

Cannot be detected

page of the EMRa below 20

a9Very highMake the score of the front
page of the EMR between 20
and 30

Very highAlmost every time

Very low

Hard to detect

8

High

Make the score of the front
page of the EMR between 30
and 40

Very high

One time every half day

Low

Seldom detected

7

High

Make the score of the front
page of the EMR between 40
and 50High

More than one time every
day

Low

Seldom detected

6MiddleMake the score of the front
page of the EMR between 50
and 60High

More than one time every
week

MiddleEasy to be detected

5MiddleMake the score of the front
page of the EMR between 60
and 70

MiddleMore than one time every
month

MiddleEasy to be detected

4MiddleMake the score of the front
page of the EMR between 70
and 80

MiddleMore than one time every
year

High

Very easy to be detected

3

Low

Make the score of the front
page of the EMR between 80
and 90Low

One time every year

High

Very easy to be detected2

Low

Make the score of the front
page of the EMR between 90
and 100

Very lowLess than one time every
year

High

Very easy to be detected1

Very low

Does not affect the score of the Extremely
front page of the EMR
low

Never

Very highNo failure modes

EMR: electronic medical record.

Table 3. Details of the expert panel.

aPosition

Rating weight

Working experience (years)

Familiarity with FMEAa

Rating weight

Physician

High

>20

General

9/10

PhysicianMiddle

10-20

General

7/10

PhysicianMiddle

1-5

Familiar

6/10

PhysicianPrimary

6-10

General

5/10

Coder

High

10-20

Familiar

9/10

Coder

Middle

6-10

Familiar

7/10

Quality control staff

High

10-20

Not very familiar

7/10

Administrator

High

>20

Not very familiar

8/10Administrator

Middle10-20

Familiar

8/10

Information engineer

Primary

1-5

Familiar

5/10

FMEA: failure mode and effects analysis.

Proposing Improvement Measures for Each Failure
Mode
According to the principle of Pareto, there were 7 causes in ourstudy that contributed to 80% of the consequences (Figure 3),
which can be addressed by revamping the information andhttps://medinform.jmir.org/2024/1/e53002

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quality management. For example, we integrated the medical
insurance system with the front page of the EMR on the sameinterface and established a set of intelligent front pages for the
EMR management system. In addition, we revamped the
management of quality, such as communicating with physicians
regularly and conducting special training seminars (Table 4).
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Figure 3. The 7 causes that contributed to 80% of the failure modes in the electronic medical record system, according to the principle of Pareto. QC:
quality control.

Table 4. Improvement measures for the causes of potential failure modes.
Why
Revamp the•information••

Revamp quality
management

•
•
•

•

a

What
The rules for quality •control are inadequate
Low accuracy inmanual quality control
The criteria are different between therequirements of patients’ insurance
and front page details

Establish a set ofintelligent front•

Improve the correctness of thefront page byphysician
Improve the professional capacity ofcodersEstablish effective
communication
channels
Form a continuous
improvement process

Formal quality control rules are simple
Insufficient professional capacity ofcodersInadequate communication between
coders and physician
Lack of a continuous improvement
process•••

How and When•pages in the EMRa
management sys- •tem•

EMR centerand information center•

Staffs of
EMR centerand information engineers•

EMR centerand clinicaldepartments•

Staffs of
EMR center,
informationengineers,and physicians•

Conducted special training
seminars•

Prepared a quality and information management
manualImplemented professional
training regularly
Invited experts for guidanceCommunicated withphysicians regularly
Provided timely feedback
to physicians on quality
control results

•
•
•

Who

Added quality control•
rules from May to
September 2021
Embedded quality control
systems into the physician’s system and front
page system from July to
October 2021
Integrated medical insurance system and the front
page of the EMR from July to December 2021

From January to May 2021

•

Where

EMR: electronic medical record.

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Comparing the Front Page of EMR Before and After
Improvement
Before carrying out improvement measures, the highest RPN
given by the experts was 296.3 for the failure mode “wrong
code for diagnosis, surgery, or operation,” which was due tothe quality rules being too simple, while the lowest was 50.6
for “basic information transmission error,” which was causedby wrong data interface or conversion error. On average, thefinal RPN was 181.2. The highest score for severity was for“wrong code for diagnosis, surgery, or operation” and the lowest
was for “expense information transmission error.” The highest
score for frequency was for “incorrectly filled-in medical
information” and the lowest was for “expense informationtransmission error.” As for detectability, the highest score wasfor “wrong diagnosis-related group code of diagnosis, surgery,or operation,” “restoration error,” and “late feedback for quality

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Zhan et alcontrol results.” The lowest score for detectability was for “basic
information transmission error.” Our team calculated the RPNof the revised front page of the EMR after implementing theimprovement measures mentioned in Table 5, and the final RPN
was 95.0, which was lower than that of the original front pageof the EMR (RPN=181.2).
The RPN of every failure mode decreased after implementing
the improvements, and the mode for the late feedback for quality
control decreased the most remarkably (Table 5). In addition,
the accuracy rate of the basic information (χ21=269.6; P<.001);
inpatient information (χ21=175.9; P<.001); diagnosis, surgery,and operation code (χ21=32.9; P<.001); and the overall accuracy
rate of the front page (χ21=239.3; P<.001) and the integrity rateof the front page (χ21=110.4; P<.001) increased significantly(Table 6).

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Table 5. Comparison of the risk analysis before and after failure mode and effects analysis model improvement.
Process, failure modes

Before FMEAa
Severity

After FMEA

Frequency

Detectability

Risk priority
numberSeverityFrequency

Detectability

Risk priority
number

3.1

3.4

4.8

50.6

3.1

3.1

3.7

35.6

Inpatient informationtransmission error

3.9

2.3

6.6

59.2

3.6

2.1

6.2

46.9

Expenses informationtransmission error

2.4

2.1

6.6

33.3

2.2

2.0

6.0

26.4

Information import from HISb
Basic informationtransmission
error

Front page filled by physicians
Incorrectly filled-in
medical information

6.1

6.6

6.4

257.7

5.8

6.2

6.0

215.8

Incorrectly filled-inother information

2.7

5.8

6.1

95.5

2.3

5.1

5.5

64.5

Front page quality control by physicians
Inadequate quality con- 6.5
trol

6.4

6.4

266.2

6.0

5.1

5.2

159.1

Inaccurate quality con- 6.1
trol

6.5

6.5

257.7

4.9

5.0

4.7

115.2

Information import from the physician’s EMRc system
Inconsistency between
the received data in
EMR system and theoriginal data

3.6

3.3

5.3

63.0

3.1

3.1

4.7

45.2

5.2

6.3

6.3

206.4

4.5

5.3

4.8

114.5

Wrong code for diagno- 6.7
sis, surgery, or operation

6.6

6.7

296.3

4.6

4.5

5.0

103.5

Restoration error

6.1

6.7

269.7

4.4

4.6

4.7

95.1

Coders’ proofreading and coding
No data errors were
found

6.6

Front page quality control by EMR management
Inadequate quality con- 6.1
trol

6.5

6.0

237.9

4.6

5.1

4.6

107.9

Late feedback of quali- 6.1
ty control results

6.4

6.7

261.6

4.7

4.9

4.6

105.9

N/A

N/A

181.2

N/A

N/A

N/A

95.0

Averagea

N/Ad

FMEA: failure mode and effects analysis.

b

HIS: hospital information system.

c

EMR: electronic medical record.

dN/A: not applicable.

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Table 6. Comparison of the accuracy and integrity of the front page of the electronic medical records before and after failure mode and effects analysis
model improvement.
Items

Front pages(n)Accuracy rate of basic
information

Accuracy rate of inpatient information

Accuracy rate of diagno- Overall accuracy
sis, surgery, andrate of front page

Integrity rate offront page

operation codea

Before48,509

94.09

95.28

97.29

93.44

96.15

After

78,890

96.09

96.74

97.81

95.48

97.26

a

Chi-square
(df)

N/A

269.6 (1)175.9 (1)32.9 (1)239.3 (1)110.4 (1)P value

N/A

<.001

<.001

<.001

<.001

<.001

N/A: not applicable.

Discussion
The quality of the front page of an EMR is quite important not
only for hospital performance management [2] but also for
insurance payments to patients [15]. Thus, it is necessary to
improve the effectiveness of the front page of the EMR. There
are many risk management tools for investigating the potential
problems in an EMR system, such as Expert Delphi [18],scenario analysis method [19], and SWOT (strengths,
weaknesses, opportunities, and threats) analysis method [20].
The advantage of Expert Delphi is that everyone's opinions arecollected and that of scenario analysis is that it identifies risks
by designing multiple possible future scenarios. The advantage
of SWOT is that it identifies the strengths, weaknesses,
opportunities, and costs of the project, thus qualitatively
identifying the project risks from multiple perspectives. FMEA
is a risk management tool that has most of the advantages of
the above tools. FMEA can not only change the occurrence ofrisk from postprocessing to preemptive prevention but is alsoa simple and a practical risk quantification method [10]. In
recent years, FMEA has been widely used in various fields,
including the medical field. Studies on medical services [21],medicine distribution [22], infection control [23], and medical
equipment operation and maintenance [24] have used FMEA
to date.
In this study, we found potential failures existing in the EMR
system of China and proposed improvement measures to solve
the problems by using FMEA. Our results showed that there
were 2 main processes and 6 subprocesses in the EMR systemthat showed 13 potential failure modes. The 2 main process
steps were management of the physician’s system andmanagement of the medical record system. The 6 subprocesses
were information transmission in the hospital information
system, front page filled by physicians, front page quality control
by physicians, information transmission in the EMR system,
coders’ proofreading and coding, and front page quality control
by EMR management. This finding is similar to that reported
in a study performed in Indonesia [25], wherein potential failuremodes included incomplete or missing medical record files,
mistakes caused by coders, and excessive code writing or
upcoding [25].
According to the principle of Pareto and from questionnaire
responses, we found that there were 7 causes in our study that
contributed to 80% of the consequences, which can be divided
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into 2 aspects for the resolution of errors. One aspect was torevamp the information by establishing a set of intelligent front
pages in the EMR management system to solve the problems
of inadequate information and inaccurate quality control and to
implement different codes of management or payment. In thisstudy, we established quality control rules for medical records
and embedded them in the system first. Accurate quality control
rules are important for maintaining data quality. For example,
Carlson et al [26] used quality control rules to identify common
logical problems, including incomplete data, invalid values, andinconsistent data, in a clinical data set of an intensive care unit.
Hart et al [27] reported >50% decrease in rejected records acrosspatient information, service information, and financial
information in 6 months by using quality rules. In addition, we
integrated the medical insurance system with the front page of
the EMR on the same interface. The other aspect was to revamp
the management of quality by conducting multichannel trainings
for doctors and coders, creating a quality and information
management manual, and communicating with physicians andcoders regularly. Previous studies [28-30] have shown a highrate of errors in physician coding for professional services,which can be risky in medical care services. One study [31]showed that clinicians and coders differ in their understandingof disease coding and need to communicate in a timely manner.
Some of our measures are also consistent with those previously
reported [25] that a hospital needs to update coding training forcoders and provide guidance and validation of coding forphysicians as well.
After implementing improvement measures, we found that the
RPN of every failure mode decreased. The most significant
decline in RPN was for the mode on the late feedback for quality
control results. Many studies have proved the benefits ofartificial intelligence. For example, machine learning could
improve the content of medical records by identifying patients'
medical information [32] or by predicting the onset of disease[33]. Therefore, we applied artificial intelligence to establish
an intelligent front page for the EMR management system andthen embedded it in the doctor's medical record writing interface
and medical record quality control interface, which made it
possible for real-time quality control of the front page. The
second indicator of decline was inaccurate quality control of
the front page by physicians. The original data on the front page,
such as basic patient information, expenses, and surgery, arefilled by physicians who decide the quality of the front pagemostly [34]. After the amendments, the accuracy and integrity
JMIR Med Inform 2024 | vol. 12 | e53002 | p. 10(page number not for citation purposes)

JMIR MEDICAL INFORMATICSof the front page were both improved for those measures, which
helped the diagnosis-related group to be more specific and theevaluation of the hospital performance more precise. In addition,
the quality of the front page of EMR is quite important forpatients. A complete front page of the medical record enables
doctors to grasp important information about the patient in ashort period, such as family history, allergy history, andimportant test results and facilitates doctors to quickly andaccurately judge the patient's condition and formulate diagnosis
and treatment plans, thereby reducing overmedication and evenerroneous medical treatment.
Human factors engineering and user-centered designs areindispensable components of mobile health technology design
and implementation [35]. Human factors emphasize humanneeds and capabilities as the core of the design technology
system, making people the most important consideration in thedesign process and aiming to achieve the goal of “making
machines fit people.” Regarding EMR system update,physicians, medical record coders, and quality controllers are
the target users, and they will resist the technology when they
believe it does not meet their expectations and needs [36]. For
this reason, this study was conducted through brainstormingand questionnaires to inform the needs of physicians, coders,and others regarding the front page of the EMR system. For
example, incorrectly filled-in medical information and quality
control proposed by physicians, coders, and other users
prompted engineers to establish a set of intelligent front pages
in the EMR management system. The usability of the EMR
system is evaluated by its effectiveness, efficiency, andsuitability for target users. Although we did not use
questionnaires to analyze the satisfaction of doctors, coders,and others with the improved EMR system, the results of FMEA
showed that RPN was greatly reduced after the system was
improved; thus, it can be hypothesized that the user's satisfaction
with the system has been enhanced. Moreover, the overall
accuracy rate of the front page (P<.001) and the integrity rate

Zhan et alof the front page (P<.001) were significantly enhanced after
implementing the improvement measures, thereby demonstrating
the increase in the effectiveness of the system. The number offront pages of EMR increased from 48,509 to 78,890 with thesame amount of time and labor, which proves that the efficiencyof the system was also improved.
Our study has several strengths. First, medical research FMEA
has mostly been performed for health care processes, hospitalmanagement, etc. For example, a study performed in Sri Lanka
used FMEA to improve medication safety in the dispensing
process [22], while another study aimed to increase theefficiency and success rate of patients with acute ischemic stroke[25]. No study has used FMEA for improving the front page of
EMR in China before. Therefore, this is the first study performed
in China, which can provide the base for future studies. Second,most studies only used FMEA to find potential failure modes
and propose improvement measures, but the system was notevaluated after the application of those measures. However, ourstudy used FMEA to compare the RPN of the front page of the
EMR before and after applying the improvement measures toverify the efficiency of the system. Our study also has some
limitations. The first limitation was that the method we used is
not advanced since there are many better methods such as dataenvelopment analysis [37] and fuzzy RPN method [38]. The
second limitation was that the process of scoring the system by
the experts was subjective although we had set weights for theexperts' scores. The third limitation was that we did not use
additional methods to validate the results, which we aim to
improve in the future. Lastly, although the EMR system called
Heren has been used in many hospitals, different hospitals may
use different types of Heren. Consequently, the generalizabilityof this study and the findings should be considered cautiously.
In conclusion, we improved the front pages of the EMRs in
China based on the potential failure modes found by the FMEA
method.

Acknowledgments
This project was supported by Project of 2023 Zhejiang Province Archives Science and Technology Project Research (2023-34).

Data Availability
Data sets are available from the corresponding author on reasonable request.

Authors' Contributions
SZ wrote the main manuscript. AS, HL, and SZ prepared the figures and tables. AS, HL, SZ, and LD designed the study. All
authors reviewed the manuscript.

Conflicts of Interest
None declared.

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Abbreviations
EMR: electronic medical record
FMEA: failure mode and effects analysis
RPN: risk priority number
SWOT: strengths, weaknesses, opportunities, and threats

Edited by J Hefner; submitted 21.09.23; peer-reviewed by C Or, Y Zhang, PH Liao; comments to author 24.10.23; revised version
received 24.11.23; accepted 05.12.23; published 19.01.24
Please cite as:
Zhan S, Ding L, Li H, Su A
Application of Failure Mode and Effects Analysis to Improve the Quality of the Front Page of Electronic Medical Records in China:
Cross-Sectional Data Mapping Analysis
JMIR Med Inform 2024;12:e53002
URL: https://medinform.jmir.org/2024/1/e53002
doi: 10.2196/53002
PMID: 38241064©Siyi Zhan, Liping Ding, Hui Li, Aonan Su. Originally published in JMIR Medical Informatics (https://medinform.jmir.org),
19.01.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,https://medinform.jmir.org/2024/1/e53002

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