A study of the difference in volume of information in chief complaint and present illness between electronic and paper medical records.
Abstract: The introduction of an electronic medical record (EMR) has been rapidly accelerating in South Korea. The EMR was expected to improve quality of care, readability, availability, and the quality of data. However, the reluctance of healthcare providers to use the EMR may have caused a reduction of information recorded in EMRs. The purpose of this study was to identify whether there was any loss of information following the introduction of a narrative text-based EMR in the recording of chief complaint and present illness in inpatient medical records. Inpatient medical records of a university hospital were retrospectively evaluated for one month before and one month after the introduction of the EMR in June 2006. The volume of information for chief complaint and present illness was measured by number of words in Korean and normalised bytes. Change in volume of information was measured by two-way ANOVA and multiple regression analyses, controlling for doctors' gender, age, and grade/year of residents, patients' readmission status, reasons for admission and service department to assess any effect of the introduction of an EMR. Total numbers of paper-based medical records (PMRs) and EMRs for analysis were 1,159 and 1,122, respectively. Forty-three doctors participated in the study. Thirty-one (72%) doctors were less than 30 years of age. Number of words proved a better outcome measure ([R.sup.2]=.22 for CC, [R.sup.2]=.36 for PI) than normalised bytes ([R.sup.2]=.18 for CC, [R.sup.2]=.35 for PI) for measuring volume of information. Results showed that the volume of information in the chief complaint and present illness was not decreased after the introduction of the EMR, except when the dependent variable was measured by number of words in the present illness. The study showed that the introduction of the EMR did not reduce the volume of information documented for chief complaint and present illness in inpatient medical records. However, further studies are needed to identify how to control the probable loss of information as showed in present illness measured by number of words.

Keywords (MeSH): Electronic Medical Records; Electronic Clinical Documentation; Hospital Information Systems; Evaluation; Quality of Health Care; Medical Record Systems, Computerised

Supplementary keyword: Health Information Management
Article Type: Report
Subject: Medical records (Usage)
Physicians (Information management)
Physicians (Technology application)
Medical care (Quality management)
Medical care (Management)
Authors: Boo, Yookyung
Noh, Young A.
Kim, Min-gyung
Kim, Sukil
Pub Date: 02/01/2012
Publication: Name: Health Information Management Journal Publisher: Health Information Management Association of Australia Ltd. Audience: Academic Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2012 Health Information Management Association of Australia Ltd. ISSN: 1833-3583
Issue: Date: Feb, 2012 Source Volume: 41 Source Issue: 1
Topic: Event Code: 200 Management dynamics; 260 General services Computer Subject: Company business management; Company systems management; Technology application
Product: Product Code: 8011000 Physicians & Surgeons NAICS Code: 621111 Offices of Physicians (except Mental Health Specialists)
Geographic: Geographic Scope: South Korea Geographic Code: 9SOUT South Korea
Accession Number: 286971364
Full Text: Introduction

Paper-based medical records (PMR) are an essential tool used by clinicians for documentation and communication in relation to patient care delivery. They are also used for medical research and by hospital administrators for management purposes. However, illegible handwriting, incomplete data, and data fragmentation have caused problems in both quality and continuity of care (Dick, Steen & Detmer 1997). If accurate and complete information is not recorded, communication between doctors can be disrupted during transfer of patients across and within care settings (Jha et al. 2009; Branger et al. 1998).

The electronic medical record (EMR) is considered to be part of the solution to solve the problems identified above by allowing doctors to enter and retrieve data directly. Efficient management of medical information, with or without electronic decision support, also contributes to improvement in the quality of care through reliable medical records (Patel et al. 2000, Chaudhry et al. 2006, Lo et al. 2007, Wright et al. 2009, Andrew & Dick 1995) Nationwide EMR projects are ongoing not only in developing countries (Williams & Boren 2008) but also in several developed countries such as the United States, United Kingdom, Australia and Canada (Jha et al. 2008). The introduction rate of the EMR has been increasing in Korea in recent years, and by 2005, inpatient EMR and ambulatory EMR had been introduced into 19.6% and 20.7% of Korean hospitals, respectively (Chae 2005).

Physician data entry is known to be one of the major issues for inputting data and utilisation of EMRs (Retchin & Wenzel 1999; Gilbert 1998; Kaplan 1994). If the quality of data in the EMR is poor this may compromise the patient and may threaten the quality of care (Chiang et al. 2003). This is particularly true in the earlier stages of implementing the EMR, when doctors who are familiar with a PMR might not enter data in the EMR (McDonald 1997; Embi et al. 2004; O'Connell et al. 2004). Unlike the PMR, entering data into the EMR is complex and onerous to some. Typing with keyboards has itself its own limitations, and reuse of pre-saved sentences or copy-and-pasting other parts of the record are major sources of errors in the EMR (Hammond et al. 2003). In order to overcome these problems, some EMRs have used a structured data entry system for the interim history, risk assessment, developmental screening and guidance sections of the record, which has resulted in better documentation in the EMR when compared with the PMR (Adams, Mann & Bauchner 2003; Johnson & Cowan 2002; Shiffman, Brandt & Freeman 1997; Bauchner Kanegaye et al. 2005). However, the patient's chief complaint and present illness were freely documented in the legacy PMR and were recorded with narrative text rather than the structured data as seen in the EMR. The use of narrative text may cause loss of information in the EMR (Patel, Arocha & Kushniruk 2002).

The aim of this study was to evaluate whether there is loss of information in the recording of chief complaint and present illness in inpatient medical records before and after the implementation of the EMR.

Method

The study hospital

The setting for study was a Korean teaching hospital with 700 beds. The hospital has approximately 300 doctors in total, including interns and residents, with annual discharges of more than 24,000. A computerised physician order entry (CPOE) system was developed in-house and has been in use since 1995; since the introduction of the EMR in July 2006, no inpatient clinical documents have been written on paper except for the Emergency Room and Intensive Care Unit. The EMR has an integrated user interface to view all the patient information in a screen. The picture archiving system (PACS) is also connected to the EMR and diagnostic images are automatically retrieved when the patient record is open. There were two types of recording in the EMR: template based and narrative text based. The templates were authored, added and modified by each department of the hospital. The Microsoft Word tool was used for the narrative text-based recording. This tool also supports special characters and images.

[FIGURE 1 OMITTED]

Study data

Medical records of inpatients discharged one month before and one month after the implementation of the EMR were reviewed and the amount of information in the chief complaint and present illness was evaluated. The chief complaint and present illness were entered in narrative text in the EMR. All medical records with the following criteria were excluded: (a) incomplete admission notes; (b) records written on both paper and EMR; (c) medical records of patients admitted before May 2006 and discharged in May 2006; (d) medical records of normal babies because the content in chief complaint and present illness is empty; and (e) all medical records of psychiatric patients because in these the distinction between chief complaint and present illness was not clear. The total number of records that satisfied the exclusion criteria was 1,159 in May (PMR) and 1,122 in June (EMR) (total n=2,281) (See Figure 1).

One registered Health Information Manager (HIM) typed the chief complaint and the present illness recorded in the paper-based medical records in Microsoft Visual Foxpro. In order to capture the exact information in the medical documents from the doctors, and to make validation easy, syntactic errors such as incorrect sentences, spelling errors, and inappropriate abbreviations were typed as they had been entered. The chief complaint and present illness with extra spaces and word breaks in EMR were also imported exactly as they appeared. Sample characteristics collected included: department type (e.g. medical or surgical department); reason for admission to differentiate diseased from injured patients; type of admission by first admission or readmission; length of stay; age; gender; and residential grade/year of doctors.

Outcome measures

The volume of information in the chief complaint and present illness were measured by both the number of words and the normalised bytes. When counting the number of words, postpositions that are attached at the end of words in Korean were not counted as a word because the Korean syntax does not allow a blank between noun and the postpositions, therefore they were allowed to be omitted without loss of meaning. The normalised bytes were calculated as a sum of bytes in the expression. Duplicates of blanks and carriage return with line feed were counted as a blank.

Participating doctors

Documentation of medical records in Korean hospitals is generally completed by first or second year resident doctors who have initially seen the patient after admission in an assigned department. The total number of working residents during the study period was 115. Forty-three of the residents participated in the study.

Statistical analysis

Unpaired t-tests were used to compare volume of information according to type of medical record, type of department, disease status, and readmission. A two-way ANOVA was used to test the difference in volume of information according to the type of medical record, type of admission, reason for admission and department. Multiple regression analyses were used to find the effect of EMR on volume of information. All factors that influenced the outcome measures in ANOVA, the participating doctor's age, gender and grade of residents were included in the multiple regression model as control factors. SAS version 9.12 was used for statistical analyses.

Results

Demographics

The sample included 1,159 PMR and 1,122 in EMR. There were no significant differences in type of admission, reason for admission, or type of department according to the type of medical record (Table 1). Forty-three doctors participated in the study. Only 12 participants (28%) were above 30 years of age. The number of male doctors was 28 (65%). Thirty-seven (86%) doctors were first- or second-year residents.

Volume of information by type of medical record

The average number of words in present illness decreased significantly from 25.95 in the PMR to 24.70 in the EMR (p=.03). However, the average number of words in chief complaint and normalised bytes in both chief complaints and present illness showed no statistical differences (Table 2).

Type of medical record had no effect on volume of information in the chief complaint when controlling for type of admission, reason for admission, and department. However, type of admission (p<.001) and department (p<.001) influenced volume of information regardless of outcome measure (Table 3).

The number of words in present illness was decreased in the EMR when the type of admission (p = .046) and department (p = .041) were controlled. However, the type of medical record had no effect on the volume of information measured by normalised bytes, regardless of controlling factors. All controlling factors influenced volume of information in every case (Table 4).

Effect of EMR on volume of information controlling extraneous factors

The multiple regression model for chief complaint had better [R.sup.2] when the volume of information was measured as the number of words ([R.sup.2]=.22) compared to normalised bytes ([R.sup.2]=.18). In both models, the type of medical record had no influence on the volume of information.

The volume of information was significantly decreased by readmission (p = .001, p = .001), medical department (p<.001), male doctor (p<.001) and length of stay (p<.01) and increased by injury (p=.01) regardless of outcome measures.

The model for present illness as with the chief complaint had a better [R.sup.2] when the volume of information was measured as the number of words ([R.sup.2]=.36) rather than normalised bytes ([R.sup.2]=.35). 1.02 words as the volume of information decreased by EMR (p=.03). However, EMR had no effect on the volume of information in normalised bytes.

Results for the other factors indicated there was a decrease in the volume of information in the surgical department (p<.001), also for female doctors (p<.002, p <.001), and disease (p<.001), with an increase in length of stay (p<.001). The readmission factor decreased in the volume of information when measured as normalised bytes (p=.005). As the age of the doctor increased, the number of words decreased (p=.04) but normalised bytes did not show a significant change (Table 5).

Discussion

Research Method

Volume of information written by doctors can vary according to each doctor's documentation in using the EMR (Treweek 2003). Most of the medical records evaluated in this study were documented by first- and second-year residents. Residents in Korea change their rotations and training institutions regularly. It was decided to restrict the study period to one month before and one month after the introduction of the EMR, in order to keep the composition of resident staff, which changes regularly, constant before and after the introduction of the EMR. We expected the composition of doctors in the study to have limited influence on the observed difference in volume of information following the implementation of the EMR. However, had the observation period been longer, the true effect of the EMR could have been extrapolated more precisely. In addition, an observation period of two months in an inpatient setting is not long enough to enable results to be generalised other hospitals.

The type of medical record and the department for chief complaints showed significant interactions in the two-way ANOVA. However, we accepted the results in spite of the interactions because the average number of words in both the EMR and the PMR of surgical department inpatients (10.16 and 9.62 respectively) had a greater average number of words than inpatients in the medical department (5.86 and 6.98 respectively). The same interpretations were applied to interactions found in present illness.

Two outcome measures were explored in multiple regression models for both chief complaint and present illness; the total number of words and normalised bytes. When the dependent variable was measured by the total number of the words, the chief complaint and present illness ([R.sup.2]=.22, [R.sup.2]=.36) provided a better explanation of the model than when it was measured by normalised bytes ([R.sup.2]=.18, [R.sup.2]=.35).

Research results

There was no significant difference in the volume of information between the PMR and the EMR except when the present illness was measured by number of words. There seem to be several reasons for this: the younger age of the doctors, the use of templates, and the increased accessibility of the medical records. The number of words in the present illness was consistently decreased in the EMR from t-test to multiple regression analyses. Because the words as terms are representations of concepts, the number of concepts to describe a patient's present illness was decreased (Patel et al. 2000; Hamilton et al. 2003). However, the average bytes per word was increased from 6.05 to 6.33 (p<.01) in the EMR (i.e. the number of characters composing each word was significantly increased in EMR). Further studies are needed to clarify whether both a decrease in the number of words and an increase in the number of characters in a word have a positive or negative effect on the quality of medical records.

While other studies have examined documentation in electronic and paper-based health records (Mikkelsen & Aasly 2000; Stausberg et al. 2003), our study is the first to compare the volume of documentation in free text input of chief complaint and present illness between electronic and paper-based medical records. When a patient's subjective information was entered with narrative text, more than twice the number of chief complaints were entered in the EMR, and fewer present illnesses were entered in the EMR than the PMR (Patel, Arocha & Kushniruk 2002). When the medical records are inputted with narrative text, a higher volume of information was entered (Apkon & Singhaviranon 2001; Callen, Alderton & McIntosh 2008) and less time was required (Apkon & Singhaviranon 2001) than the PMR. Even when comparing results of the PMR with the EMR equipped with structured data entry format, the volume of information in EMR was more than the PMR (Tang, Larosa & Gorden 1999; Rosenbloom et al. 2004; Roukema et al. 2006; Tsai & Bond 2008).

Limitations of the study

This study was conducted in one Korean teaching hospital. If the same study was to be conducted in other hospitals, the characteristics of the EMR and the settings of different hospitals might provide different results. The medical records are written in Korean. Even though English terms are used together with Korean in the medical records, the syntax of sentences in this study was based on the Korean language (e.g. the combination of preposition and noun as objective in English is counted as a word composed of a noun with postposition in Korean). These issues should be considered when the results of this study are compared to other studies with non-Korean languages.

Conclusion

The volume of information in chief complaints was the same in both the PMR and EMR regardless of outcome measure. It also remained the same when measured by the normalised bytes. However, the volume of information in present illness decreased by 1.02 words when measured by number of words. Therefore, we can conclude that the volume of information in the EMR was not decreased. Further studies are needed to explore how to control the probable loss of information in the recording of present illness as measured by the number of words and how to improve the quality of electronic documentation of care generally.

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Yookyung Boo PhD

Eulji University of Korea

College of Health Industry

Department of Healthcare Management

Gyeonggi-do, Korea

Young A Noh MPH

The Catholic University of Korea

Graduate School of Public Health

Seoul, Korea

Min-gyung Kim MBA

The Catholic University of Korea

College of Medicine

Department of Preventive Medicine

Seoul, Korea

Corresponding Author:

Sukil Kim MD, PhD, MSc

Department of Preventive Medicine

The Catholic University of Korea, College of Medicine

505 Banpo-dong Seocho-gu, Seoul 137-701 Korea

Tel: +82 2 2258 736 7

Fax: +82 2 532 3820

Mobile: +82 10 9911 0605

email: sikimMD@catholic.ac.kr
Table 1: General characteristics of medical records
according to type of medical records.

FACTORS                        TYPE OF MEDICAL RECORD (%)

                          PMR            EMR            TOTAL
Type of admission
  First admission        734(51.0%)     705(49.0%)   1,439(100%)
  Readmission            425(50.5%)     417(49.5%)     842(100%)
Reason for admission
  Disease              1,067(50.9%)   1,029(49.1%)   2,096(100%)
  Injury                  92(49.7%)      93(50.3%)     185(100%)
Type of department
  Medical                680(51.6%)     639(48.4%)   1,319(100%)
  Surgical               479(49.8%)     483(50.2%)     962(100%)
Length of stay           4.98 days       5.05 days
Total                  1,159(50.8%)   1,122(49.2%)   2,281(100%)

PMR: Paper-based medical record, EMR: Electronic medical record

Table 2: Average volume of information according to
type of medical records and outcome measures

OUTCOME                          TYPE OF MEDICAL RECORD
MEASURES                           (MEAN [+ or -] SD)

CONTENT                       PMR                     EMR

Number of words
  Chief Complaints    8.07 [+ or -] 5.88       7.71 [+ or -] 5.81
  Present Illness    25.95 [+ or -] 13.84     24.70 [+ or -] 14.16
Normalized bytes
  Chief Complaints   54.68 [+ or -] 44.21     54.86 [+ or -] 45.25
  Present Illness   162.80 [+ or -] 98.22    162.46 [+ or -] 100.78

OUTCOME
MEASURES

CONTENT                  t       p

Number of words
  Chief Complaints    -1.47    .14
  Present Illness     -2.15    .03
Normalized bytes
  Chief Complaints      .10    .92
  Present Illness      -.08    .93

PMR: Paper-based medical record, EMR: Electronic medical record

Table 3: Volume of information in chief complaint
according to related factors by outcome measures

                                   OUTCOME MEASURES
                                  (MEAN  [+ or -] SD)

                    TYPE OF                  NUMBER
                    MEDICAL                    OF
FACTORS (A)         RECORD      n             WORDS

Type of admission
  First admission     EMR       705    8.07 [+ or -] 5.61
                      PMR       734    8.67 [+ or -] 6.13
  Readmission         EMR       417    7.10 [+ or -] 6.09
                      PMR       425    7.04 [+ or -] 5.28
                                        [P.sub.A] < .001,
                                        [P.sub.b] = .29,
                                        [P.sub.A*B] = .19
Reason for Admission
  Disease             EMR      1,029   7.65 [+ or -] 6.00
                      PMR      1,067   8.07 [+ or -] 6.09
  Injury              EMR        93    8.35 [+ or -] 2.73
                      PMR        92    8.03 [+ or -] 2.44
                                        [P.sub.A] = .46,
                                        [P.sub.B] = .91,
                                        [P.sub.A*B] = .41
Department
  Medical             EMR       639    5.86 [+ or -] 4.64
                      PMR       680    6.98 [+ or -] 4.91
  Surgical            EMR       483    10.16 [+ or -] 6.27
                      PMR       479    9.62 [+ or -] 6.75
                                        [P.sub.A] < .001,
                                        [P.sub.B] = .22,
                                       [P.sub.A*B] < .001

                                OUTCOME MEASURES
                               (MEAN  [+ or -] SD)

                    TYPE OF
                    MEDICAL        NORMALISED
FACTORS (A)         RECORD           BYTES

Type of admission
  First admission     EMR     57.83 [+ or -] 43.81
                      PMR     58.86 [+ or -] 45.16
  Readmission         EMR     49.84 [+ or -] 47.21
                      PMR     47.47 [+ or -] 41.58
                               [P.sub.A] < .001,
                                [P.sub.b] = .73,
                               [P.sub.A*B] = .38
Reason for Admission
  Disease             EMR     54.92 [+ or -] 46.75
                      PMR     54.65 [+ or -] 45.74
  Injury              EMR     54.15 [+ or -] 22.9
                      PMR     55.07 [+ or -] 19.03
                                [P.sub.A] = .96,
                                [P.sub.B] = .93,
                               [P.sub.A*B] = .86
Department
  Medical             EMR     43.93 [+ or -] 41.43
                      PMR     50.07 [+ or -] 41.81
  Surgical            EMR     69.32 [+ or -] 46.06
                      PMR     61.22 [+ or -] 46.68
                               [P.sub.A] < .001,
                                [P.sub.B] = .60,
                               [P.sub.A*B] < .001

[P.sub.A] : p-value for each factor A, [P.sub.B] : p-value for factor
B (type of medical records)

[P.sub.A*B] : p-value for interaction between factor A and factor B

Table 4: Volume of information in present illness
according to related factors by outcome measures

                                     OUTCOME MEASURES (MEAN
                                         [+ or -] SD)

                     TYPE OF                     NUMBER
                     MEDICAL                       OF
FACTORS (A)         RECORD (B)     n             WORDS

Type of admission
  First admission      EMR         705    24.02 [+ or -] 14.16
                       PMR         734    25.45 [+ or -] 14.25
  Readmission          EMR          417   25.83 [+ or -] 13.59
                       PMR         425    26.82 [+ or -] 13.01
                                            [P.sub.A] = .01,
                                           [P.sub.B] = .046,
                                           [P.sub.A*B] = .71
Reason for Admission
  Disease              EMR        1,029   25.66 [+ or -] 14.26
                       PMR        1,067   26.83 [+ or -] 13.93
  Injury               EMR          93    13.99 [+ or -] 6.72
                       PMR          92    15.82 [+ or -] 7.29
                                           [P.sub.A] < .001,
                                            [P.sub.B] = .15,
                                           [P.sub.A*B] = .75
Department
  Medical              EMR         639    31.91 [+ or -] 12.57
                       PMR         680    32.86 [+ or -] 12.34
  Surgical             EMR         483    15.14 [+ or -] 9.85
                       PMR         479    16.15 [+ or -] 9.15
                                           [P.sub.A] < .001,
                                            [P.sub.B] = .04,
                                           [P.sub.A*B] = .96

                                   OUTCOME MEASURES
                                  (MEAN [+ or -] SD)

                     TYPE OF
                     MEDICAL           NORMALISED
FACTORS (A)         RECORD (B)           BYTES

Type of admission
  First admission      EMR       157.62 [+ or -] 102.22
                       PMR       161.20 [+ or -] 102.15
  Readmission          EMR       170.63 [+ or -] 97.89
                       PMR       165.57 [+ or -] 91.08
                                    [P.sub.A] = .04,
                                    [P.sub.B] = .86,
                                   [P.sub.A*B] = .32
Reason for Admission
  Disease              EMR       169.25 [+ or -] 101.53
                       PMR       168.69 [+ or -] 99.07
  Injury               EMR        87.25 [+ or -] 48.46
                       PMR        94.53 [+ or -] 51.57
                                   [P.sub.A] < .001,
                                    [P.sub.B] = .65,
                                   [P.sub.A*B] = .60
Department
  Medical              EMR       212.70 [+ or -] 90.31
                       PMR       210.06 [+ or -] 90.04
  Surgical             EMR        95.98 [+ or -] 71.07
                       PMR        95.70 [+ or -] 64.53
                                   [P.sub.A] < .001,
                                    [P.sub.B] = .67,
                                   [P.sub.A*B] = .73

[P.sub.A] : p-value for each factor A, [P.sub.B] : p-value for factor
B (type of medical records)

[P.sub.A*B] : p-value for interaction between factor A and factor B

Table 5: Effect of EMR on the volume of information in
chief complaint and present illness controlling related
factors by outcome measures

                                      NUMBER OF        NORMALISED
                                        WORDS             BYTES

INDEPENDENT VARIABLES               [beta]     P      [beta]     P

  Chief complaint
  EMR(PMR=0)                         -.17      .43      1.75     .31
  Readmission(First admission=0)     -.65     .004     -5.72    .001
  Medical dept.(Surgical dept.=0)   -4.71    <.001    -27.03   <.001
  Male doctor(Female doctor=0)      -3.58    <.001    -25.71   <.001
  Injury(disease=0)                  1.51    <.001     10.87    .001
  Age of doctor                      -.40    <.001     -3.61   <.001
  Length of stay                      .07     0.01       .53     .01
                                    ([R.sup.2]=.22)   ([R.sup.2]=.18)
Present illness
  EMR(PMR=0)                        -1.02     .03        .53     .88
  Readmission(First admission=0)     -.96     .05     -10.00    .005
  Medical dept.(Surgical dept.=0)   16.71   <.001     119.52   <.001
  Man doctor(Woman doctor=0)         1.68   <.002      24.86   <.001
  Injury(disease=0)                  4.13   <.001      30.55   <.001
  Age of doctor                      -.23     .04       -.17     .83
  Length of stay                      .22   <.001       1.74   <.001
                                    ([R.sup.2]=.36)   ([R.sup.2]=.35)
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