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蹂뫢룹쓽썝 궡 냼洹쒕え 寃궗떎쓽 젙룄愿由 떎깭議곗궗 諛 빐寃곗븞 紐⑥깋
Survey and Solutions for the Current Status of Quality Control in Small Hospital Laboratories
J Lab Med Qual Assur 2018;40:101-108
Published online June 30, 2018
© 2018 Korean Association of External Quality Assessment Service.

源옱븳1, 理쒖吏1, 솴洹쒗쁽2, 씠옱샇2, 븳誘몄닚1
Jae Han Kim1, Eun Jin Choi1, Gyuhyeon Hwang2, Jae Ho Lee2, Mi-Soon Han1

1옣썝쓽猷뚯옱떒 쑀닾쓽썝 吏꾨떒寃궗쓽븰怨,
2닚쑀닾諛붿씠삤 IT 뿰援ъ냼

1Department of Laboratory Medicine, U2 Clinical Laboratories, Jangwon Medical Foundation, Korea
2Department of IT Research Institute, U2Bio Co. Ltd., Seoul, Korea
Correspondence to: Mi-Soon Han Department of Laboratory Medicine, U2 Clinical Laboratories, Jangwon Medical Foundation, 68 Geoma-ro, Songpa-gu, Seoul 05755, Korea Tel: +82-2-910-2103 Fax: +82-2-910-2190 E-mail: loveelohim9083@hanmail.net
援먯떊옄: 븳誘몄닚 슦)05755 꽌슱떆 넚뙆援 嫄곕쭏濡 68, 옣썝쓽猷뚯옱떒 쑀닾쓽썝 吏꾨떒寃궗쓽븰怨 Tel: 02)910-2103, Fax: 02)910-2190, E-mail: loveelohim9083@hanmail.net
Received January 22, 2018; Revised March 26, 2018; Accepted March 15, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract

諛곌꼍:

쓽븰쟻쑝濡 以묒슂븳 삤瑜섎 諛⑹븯湲 쐞빐 쓽猷뚭린愿쓽 寃궗떎뿉꽌뒗 泥좎븯寃 넻怨꾩쟻씤 젙룄愿由 젅李⑤ 닔뻾빐빞 븳떎. 슦由щ뒗 냼洹쒕え 寃궗떎쓽 젙룄愿由 떎깭瑜 議곗궗븯怨, 씠뱾쓽 젙룄愿由 媛쒖꽑쓣 쐞빐 돺寃 궗슜븷 닔 엳뒗 깉濡쒖슫 넻怨꾩쟻 젙룄愿由ы봽濡쒓렇옩瑜 媛쒕컻븯떎.

諛⑸쾿:

쇅遺 궡遺젙룄愿由 떎깭瑜 臾삳뒗 꽕臾몄瑜 젣옉븯뿬 냼洹쒕え 寃궗떎씠 엳뒗 蹂묒썝 諛 쓽썝뿉 꽕臾몄“궗瑜 슂泥븯떎. 삉븳 臾대즺濡 떎슫濡쒕뱶븯뿬 꽕移섎쭔 븯硫 넀돺寃 궗슜븷 닔 엳뒗 젙룄愿由 냼봽듃썾뼱瑜 媛쒕컻븯떎.

寃곌낵:

쓽썝쓽 32% (66湲곌 以 22泥) 蹂묒썝쓽 52% (23湲곌 以 12泥)뿉꽌 쇅遺젙룄愿由щ 닔뻾븯怨 엳뿀떎. 빐떦 臾명빆뿉 떟븳 湲곌쓽 73% (90湲곌 以 66泥)뒗 궡遺젙룄愿由ъ뿉 븳 궡遺 吏移⑥씠 엳쑝硫 씠瑜 쐞빐 긽뾽솕맂 젙룄愿由щЪ吏덉쓣 궗슜븯怨 엳뿀떎. 洹몃윭굹 쓽썝쓽 52% 蹂묒썝쓽 23%留뚯씠 젣議곗궗媛 젣떆븳 뿀슜踰붿쐞 떊 옄泥 꽕젙븳 뿀슜踰붿쐞瑜 궗슜븯怨 엳뿀떎. 삉븳 쓽썝 52%, 蹂묒썝 82%뿉꽌 젙룄愿由 痢≪젙媛믪쓣 닔湲곕줈 愿由ы븯怨 엳뿀떎. 젙룄愿由 뾽臾댁뿉 엳뼱 씠윭븳 臾몄젣젏쓣 媛쒖꽑븷 닔 엳룄濡 슦由щ뒗 넻怨꾩쟻 젙룄愿由ъ쓽 紐⑤뱺 湲곕낯 湲곕뒫쓣 쟾궛솕떆궗 닔 엳뒗 깉濡쒖슫 젙룄愿由ы봽濡쒓렇옩쓣 媛쒕컻븯떎.

寃곕줎:

꽕臾몄“궗瑜 넻빐 냼洹쒕え 寃궗떎쓽 쇅遺 諛 궡遺젙룄愿由 쁽솴뿉 븳 湲곗큹 옄猷뚮 뼸뿀떎. 뜑 굹븘媛 슦由щ뒗 깉濡 媛쒕컻맂 젙룄愿由ы봽濡쒓렇옩씠 냼洹쒕え 寃궗떎쓽 젙룄愿由 吏덉쓣 媛쒖꽑븯뒗 뜲 떎젣쟻씤 룄씠 맆 寃껋씠씪怨 젣븞븳떎.

Background:

To prevent medically significant errors, hospital laboratories must perform thorough statistical quality control (QC) procedures. We surveyed the QC status of small laboratories and created new statistical QC software that they can easily use for improving QC.

Methods:

A questionnaire on the status of external and internal QC was created and sent to clinics and hospitals with small-scale laboratories. We then created QC software that can be downloaded and installed for free.

Results:

External quality assessments were performed in 32% of the clinics (22 of 66) and 52% of the hospitals (12 of 23). Seventy-three percent of all institutions (66 of 90) carried out an internal quality assessment based on their own guidelines, mostly using commercialized QC materials. However, only 52% of clinics and 23% of hospitals used their own acceptable range instead of the manufacturer셲 expected range. In addition, the proportion of manual QC management reached 52% in clinics and 82% in hospitals. The QC software we designed covers all the basic functions of statistical QC and aims to improve the quality of laboratories.

Conclusions:

We obtained basic data on the current status of external and internal QC in small-scale laboratories using this survey. Furthermore, we suggested that new QC software may actually help to improve QC of small laboratories.

Keywords : Hospital laboratories, Quality control, Software, Health care quality assessment
INTRODUCTION

Laboratory medicine plays an essential role in disease diagnosis in the medical field. It allows us to measure specific components of biological samples from the human body, thereby diagnosing diseases, determining therapeutic effects, and predicting prognosis. As a rule, the more advanced the diagnostic test is, the more likely that the patient셲 medical care will be reliant on the test results [1]. For this reason, laboratories must perform strict quality control (QC) procedures to avoid reporting erroneous results.

Statistical quality control (SQC) techniques are considered the best approach to assure quality in laboratories [2]. Large-scale laboratories are now performing complex SQC procedures using commercially available software via specific providers or proprietary software developed through costly outsourcing. In contrast, small-scale laboratories often have insufficient labor or funding to automate QC processes. The QC management in small-scale laboratories is performed manually using excel spreadsheets, not using computerized QC software. This implies the possibility that internal QC is not performed properly or produces frequent errors because of manual operation.

We surveyed the QC status of clinics and hospitals with small-scale laboratories, which are expected to have problems with achieving adequate QC. We also attempted to design new QC software that consists of basic essential functions and is simple and free to use, specifically for small-scale laboratories.

MATERIALS AND METHODS

1. Status Survey

1) Sample

In this study, we defined a small-scale laboratory as any laboratory where the number of clinical laboratory technologists was five or less. We randomly selected 228 out of all our clients (1,824 clinics or hospitals), adopting a stratified sampling strategy. All institutions were divided into five groups according to their region, and the number of survey participants in each group was allocated in proportion to the number of affiliated institutions. We explained the purpose of the study and the intended use of the collected data to the head of each institute. Then, this survey was conducted with informed consent. In addition, all private information of the institution was anonymized.

2) Questionnaire

The questionnaire comprised 21 items asking about the status of external and internal QC assessment of the institute as well as general information.

2. Development of New Internal Quality Control Software

1) Intended use

We planned for the new QC software to handle both quantitative and semi-quantitative measurement procedures to help small laboratories improve their QC processes. This program automated several SQC processes, thereby allowing users to quickly and easily perform various QC tasks. It met the requirements of the Korean Laboratory Accreditation Program of the Korea Laboratory Medicine Foundation [3].

2) Development tools

The QC software was created using the Microsoft Visual Studio 2010 Integrated Development Environment Visual C#, in the language C# 3.0. We designed the software within .Net Framework 3.5, and used a graphical user interface development environment to produce an application formatted for Windows XP or higher versions. Two expert software engineers in the medical field participated in this process.

RESULTS

1. Questionnaire

We received responses from 98 of the 228 institutes (43.4%) that we visited for the survey during 2 weeks in May 2017. Specific respondents were as follows: 72 clinical laboratory technologists (73.5%), 18 nurses (18.3%), four doctors (4.1%), and four non-respondents (4.1%). Of the 98 institutes that responded, 73 were clinics (74.5%) and 24 were hospitals (24.5%). One institute did not provide this information.

1) General information

Among the clinics with a small laboratory, the medical specialties were occupied as follows, in descending order of frequency: internal medicine (61.6%), orthopedic surgery and neurology (each 6.8%), urology (4.1%), general surgery (2.7%), and others. For the hospitals, the first and second ranked specialties were the same as for clinics (internal medicine, 95.8%; orthopedic surgery, 58.3%). From the third rank on, however, medical specialty differed: anesthesiology (25.0%), general surgery (20.8%), neurosurgery and radiology (each 12.5%), family medicine (8.3%), and others.

Regarding the electronic medical record (EMR) systems used, Ysarang (UBcare, Seoul, Korea) was the most common, used by 74 institutes (78.7%). Other institutes used Dr. Brain (Brain Consulting, Seoul, Korea) (5.4%), Phoenix (Ginus, Seoul, Korea), and eGHIS (MCC, Seoul, Korea) (each 2.7%). For 79 of the 91 institutes (86.8%), test results automatically interfaced with the EMR system. For the rest, the test results were input into the EMR system manually.

2) Laboratory size

We classified the clinics and hospitals into subclasses based on the average number of tests per month. Approximately half (46.5%) of the clinics performed 竊100 to 竊500 tests per month, and more than half of these tests were for the hematology and chemistry sections of the laboratory. More than half the hospitals (52.2%) performed 竊500 tests per month. Besides the hematology and chemistry sections, these laboratories performed tests in the urinalysis, electrolyte, immunology, and coagulation sections. When comparing equivalent groups in terms of number of monthly tests between clinics and hospitals, hospitals operated more inspection sections and equipment than those of the clinics. However, the average number of clinical laboratory technologists did not significantly differ between the two types of institutions (Table 1).

Table 1 . The size of the clinical laboratories of the surveyed institutes.

꺿긘ariableNo. of institutes (%)No. of monthly testsNo. of instruments꺿꺿긕ections*No. of clinical laboratory technologist
Clinics (n=71)
14 (19.7)1 to <501.4짹0.9HA1.0짹0.0
13 (18.3)50 to <1001.8짹0.4HA, CM1.1짹0.4
33 (46.5)100 to <5002.8짹1.2HA, CM1.1짹0.3
11 (15.5)돟5002.9짹1.1HA, CM, UA1.3짹0.6
Hospitals (n=23)
3 (13.0)50 to <1003.7짹1.5HA, CM, UA, ET1.3짹0.6
8 (34.8)100 to <5003.3짹2.0HA, CM, UA, ET, IM1.2짹0.4
12 (52.2)돟 5003.8짹1.7HA, CM, UA, ET, IM, CA1.5짹1.0

Abbreviations: HA, hematology; CM, chemistry; UA, urinalysis; ET, rlectrolyte; IM, immunology; CA, voagulation.

*Performed in at least half of the institutes in each group.


3) Status of external quality assessment

A total of 89 institutes responded to the questions on external quality assessment (EQA). Thirty-three percent (22 out of 66) of the clinics and 52% (12 of 23) of the hospitals answered that they performed EQA. While the primary method of EQA in clinics was peer comparison, hospitals mainly participated in the domestic proficiency testing program (Table 2).

Table 2 . The status of external quality assessment according to the number of tests.

꺿긘ariableEQA rateNo. of monthly testsManaged test proportion*EQA method

Proficiency testingPeer comparison
Clinics (n=22)
0 (0/14)1 to <50
25 (3/8)50 to <100110033 (1/3)67 (2/3)
36 (12/33)100 to <500110018 (2/11)81 (9/11)
64 (7/11)돟5002610057 (4/7)43 (3/7)
Total33 (22/66)33 (7/21)67 (14/21)
Hospitals (n=12)
67 (2/3)50 to <1007610050 (1/2)50 (1/2)
75 (6/8)100 to <5005110066 (4/6)33 (2/6)
33 (4/12)돟50017575 (3/4)25 (1/4)
Total52 (12/23)67 (8/12)33 (4/12)

Values are presented as application % (number of 쐙es responses/total number of respondents) or range %, unless otherwise stated.

Abbreviation: EQA, external quality assessment.

*Whole range of response in terms of percentage of EQA performed in the entire test.

Including one institute that performed this simultaneously with peer comparison.


4) Status of internal quality assessment

The respondents of 73% (66 out of 90) of institutes answered that they utilized internal quality assessment (IQA) for self-management of quality in their laboratories. The status of IQA in these 66 institutes (including 44 clinics and 22 hospitals) is detailed in Table 3. The majority of institutes conducted internal QC using commercialized QC materials. However, only 52% of the clinics and 23% of the hospitals used their own acceptable ranges, rather than the manufacturer셲 expected range. The overall percentage of manual QC management was 52% for clinics and 82% for hospitals. Here, we defined manual QC management as manually entering the QC results into the QC software or simply printing and storing QC results from the equipment (Table 3).

Table 3 . The status of internal quality assessments according to the number of tests.

꺿꺿긘ariableClinics (n=44)Hospitals (n=22)


1 to <5050 to <100100 to <500돟50050 to <100100 to <500돟500
Self-designed guidelines21 (3/14)50 (5/10)75 (25/32)100 (11/11)100 (3/3)88 (7/8)100 (12/12)
Commercialized QC materials67 (2/3)100 (5/5)96 (24/25)100 (11/11)100 (3/3)86 (6/7)92 (11/12)
Self-set QC limits0 (0/3)40 (2/5)40 (10/25)100 (11/11)67 (2/3)29 (2/7)18 (1/12)
QC software
귽ommercial product33 (1/3)20 (1/5)41 (9/22)27 (3/11)29 (2/7)8 (1/12)
긌aboratory development40 (2/5)50 (11/22)14 (1/7)8 (1/12)
긂xcel program20 (1/5)9 (2/22)55 (6/11)100 (2/2)43 (3/7)75 (9/12)
긐ther33 (1/3)20 (1/5)18 (2/11)14 (1/7)8 (1/12)
Manual QC management67 (2/3)40 (2/5)52 (13/25)55 (6/11)100 (2/2)100 (7/7)75 (9/12)

Values are presented as application % (number of 쐙es responses/total number of respondents).

Abbreviation: QC, quality control.


5) Actual obstacles to correcting internal quality assessment

The barriers to performing IQA for clinics are listed as follows, in order of perceived importance: high cost, work complexity, insufficient workforce, time shortage, and lack of a proper QC software program. The barriers faced by hospitals were almost identical to those faced by clinics, except that insufficient workforce was considered more important than work complexity (Table 4).

Table 4 . Practical obstacles in internal quality assessment.

꺿꺿귻arrierClinics (n=62)Hospitals (n=23)
Cost burden30 (48.4)18 (78.3)
Insufficient workforce16 (25.8)8 (34.8)
Time shortage12 (19.4)6 (26.1)
Work complexity20 (32.3)5 (21.7)
Absence of programs11 (17.7)3 (13.0)
Other5 (8.1)2 (8.7)

Values are presented as number (%).


6) Preferences for free quality control software

When asked if they would like to use free QC software, 31 of the 61 clinics (50.8%) and 16 of the 22 hospitals (72.7%) answered 쐙es. They suggested the following features as essential for free QC software: simple and easy to use, interface displaying QC results, automatic judgment according to Westgard multiple rules, help with corrective action, Levy-Jennings charts with outliers highlighted, statistical calculations (means, standard deviations, coefficient variations), ability to set self-control limits, and generation and printing of QC reports.

2. New Internal Quality Control Software

From October 2016 to February 2017, we planned the development of a new program and prepared the necessary environment for it. Then, over the second half of 2017, we created new QC software called SmartQC ver. 1.0 (U2Bio Co. Ltd., Seoul, Korea).

1) SmartQC installation

(1) Install the Microsoft SQL Server 2008 R2 SP2 (Express Edition) and set up the network (https://www.microsoft.com/ko-kr/download/details.aspx?id=30438).

(2) Configure the PC Firewall using the New Inbound Rule Wizard.

(3) Download the SmartQC software (http://www.lab2gene.co.kr/Contents/Customer/NoticeList.aspx?categoryID=5).

(4) Run the following file: LaboratoryGatewayIILauncher.exe 넂 DBRestore.exe.

(5) Check the server information on connection by pressing the (Log in) button.

(6) Create an account after logging in using the default ID and password (smartqc/smartqc).

(7) Input the basic inspection information such as the instrument, analyte, and QC materials.

For more information, installation and operating manuals can be downloaded from the SmartQC software website. If the instrument셲 QC raw data is linked to the SmartQC software, almost all IQA tasks can be run on the computer.

2) Basic functions

The Basic Data menu of the SmartQC program has several specific functions, such as user manager, inspection manager, QC material manager, instrument manager, and department manager (Fig. 1). The QC lot manager menu allows users to manage the names of materials, lot numbers, expiration data, and the expected ranges for QC materials. In the Daily Task menu, daily QC results for each level are recorded numerically, plotted as a Levy-Jennings chart, and judged using the Westgard multiple rules. It is also possible to set a temporary and formal QC allowable range for each laboratory and to output the results as a report. Two review reports can be generated in both Microsoft Excel and PDF formats: monthly QC statistics per test item and a monthly precision trend of all items per instrument. Additional advantages of this software are that it will be continuously updated by IT specialists and can link QC results with instruments via installation of the interface program.

Figure 1.

Screenshot of the SmartQC software. Five different menu tabs are provided, as follows: SmartQC (user mana짯ger), basic data (inspection manager, QC material manager, instrument manager, and department manager), QC lot manager, test results (daily task and target manager), and report (daily report and monthly report). Abbreviation: QC, quality control.


DISCUSSION

Many of the institutes in this study operated the medical specialties of internal medicine or orthopedic surgery, and the laboratories primarily ran tests in the hematology and chemistry sections. There was a trend for hospitals to run more tests, more inspection sections, and to have more instruments than clinics, even though the number of monthly tests was similar overall. The number of clinical laboratory technologists did not significantly differ between clinics and hospitals, suggesting that hospitals tend to manage staff tightly.

Both clinics and hospitals commonly had low EQA rates, suggesting that the accuracy of QC tests is not regularly verified. For small-scale laboratories, it might be necessary to first recommend periodic peer comparisons to increase EQA rates [2]. This is because peer comparison tests do not require an additional participation fee, and the procedure and analysis are not complicated. When using commercialized QC materials, laboratories can participate in peer comparison programs provided by QC material manufacturers as an alternative EQA procedure [4].

As for IQA, many of the institutes (48% of clinics and 77% of hospitals) use the expert ranges given on manufacturers assay sheets and do not create their own acceptable ranges for QC results. Manufacturer ranges might be too broad to detect problems during inspections of individual laboratories [5]. According to the results of the 2017 QC Practices Survey, conducted by Westgard QC, 43.1% of 701 laboratories across the world and 32.1% of the 271 US laboratories used manufacturer ranges listed on package inserts as expected ranges [6,7]. Furthermore, among these respondents, public health or government laboratories and reference laboratories constituted around 56.0% of the global labs and 32.2% of the US labs. If respondents of private hospital labs and outpatient clinic labs are analyzed separately, we might assume that the usage of manufacturer ranges will be higher. Accordingly, we can perhaps conclude that the results for the clinics in this study do not significantly differ for the United States or even the world. However, the percentage of hospitals that used the manufacturer range was much higher compared to that of the clinics. This is presumably because of the insufficient workforce in proportion to the number of tests or inspection sections, such that only basic QC tasks were performed.

In addition, the proportion of institutes that manually managed the QC results was well over half of the total. Manual management makes it difficult to perform IQA correctly and thoroughly. We identified a need for continued improvement in overcoming practical barriers to QC in small laboratories. However, major structural obstacles such as cost, workforce, and time problems are difficult to solve quickly. The complexity of the QC tasks is expected to be gradually resolved through education, because insurance policies in South Korea have begun mandating the completion of quality management training for institutes without full-time specialists in anatomic pathology. The lack of appropriate QC software has been regarded as less important compared to other factors. However, providing new QC software that can alleviate the burden of other obstacles might be practically helpful for managing the internal quality of small laboratories.

We obtained basic data on the current status of external and internal QC in small-scale laboratories through this survey. The accuracy of this survey is limited because respondents might have been reluctant to disclose the QC status of the laboratory and understanding of the questions might have been somewhat different depending on the respondent셲 job type. If this kind of survey is mandated and continued by the relevant agency, we expect that it will help to bring about improvements in QC in small laboratories. We expect that these data will be helpful in identifying problems in QC procedures and improving small-scale laboratories. Furthermore, we developed a new QC software specifically for these laboratories for improving QC.

ACKNOWLEDGEMENTS

This work was supported by the 2017 Academic Research Fund of the Korean Association of Quality Assurance for Clinical Laboratories (survey research, assignment number 2017-06).

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