Published on in Vol 20, No 8 (2018): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10886, first published .
Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

Original Paper

1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan

2Department of General Surgery, Chi-Mei Medical Center, Tainan, Taiwan

3Information Technology Office, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan

*these authors contributed equally

Corresponding Author:

Yu-Ting Yeh, RN, PhD

Graduate Institute of Biomedical Informatics

College of Medical Science and Technology

Taipei Medical University

250 Wuxing Street

Taipei,

Taiwan

Phone: 886 22490088 ext 8901

Email: yuting@tmu.edu.tw


Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases.

Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness.

Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity.

Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively.

Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

J Med Internet Res 2018;20(8):e10886

doi:10.2196/10886

Keywords



Electronic laboratory reporting (ELR) generally refers to the automated transmission of reportable laboratory findings from public health services, hospitals, and other laboratories to local or state public health agencies. Many communicable diseases currently under nationwide surveillance can be identified and confirmed by laboratory test results. Thus, ELR has become a critical part of the disease surveillance process. Previous studies have shown that ELR improves the timeliness, accuracy, and completeness of reported laboratory data, which, in turn, improves the effectiveness and efficiency of public health responses to outbreaks and cases of notifiable conditions [1-4]. It has been included as a meaningful use objective by the US Centers for Medicare and Medicaid Services’ Electronic Health Records Incentive programs [5,6]. However, ELR often lacks the clinical information needed to satisfy a case definition, such as disease signs, symptoms, and diagnoses. Moreover, ELR is usually nonspecific, particularly in cases (such as acute hepatitis B) where diagnoses require integration of laboratory tests and clinical information [7-10]. The lack of specificity in ELR increases the workload for health departments compelled to investigate suggestive but nonspecific lab results [11].

An electronic medical record (EMR) is a systematized collection of computerized patient and population health information. An EMR supports secure, real-time, point-of-care, patient-centric information availability and is a resource for clinical care as well as research and education. Recently, EMR systems have become an increasingly pervasive technology in health care settings [12-14]. Several studies have shown that EMR systems can accelerate clinical information flow, facilitate health care data integration, and improve the efficiency and quality of medical services [15-17]. With the growing adoption of EMR systems in recent years, increasingly sophisticated data have become available in EMRs to support infection surveillance, prevention, and control [18-20]. Using EMR data for the detection and reporting of infectious diseases also has the potential to improve public health monitoring and reporting [21]. However, current EMR systems are primarily built to serve clinical practice and are not structured for public health use. Laboratory test data transmitted to EMR systems might not be as complete and as timely as data received directly from a laboratory information system (LIS) [22].

In Taiwan, as required by law [23], clinical laboratory units operated by hospitals, health agencies, and research institutes must report the outcomes of tests for pathogens, mostly bacteria and viruses, that meet the notification conditions of Taiwan’s Centers of Disease Control (TCDC) in order to enable epidemiological surveillance and advanced alerting of communicable diseases. Thus, the TCDC has developed a Web-based Notifiable Disease Reporting (WebNDR) system to strengthen infectious disease control and surveillance. The system requires surveillance professionals to enter detailed information of notifiable diseases and laboratory tests manually if the diseases meet the TCDC’s reporting definitions. Unfortunately, these reporting operations are time consuming and error prone [24]. This is also the case for unannounced or delayed notifications [25].

In order to reduce the workload of surveillance professionals, and address the lack of specificity of ELR, the TCDC launched a pilot project, called Automated Laboratory Reporting (ALR), in 2014 [26]. The ALR system enables a hospital to automatically transmit reportable cases to the TCDC when they meet TCDC’s notifiable conditions. Reportable cases include not only detailed laboratory test data but also relevant EMR data. Currently, hospitals can join the project on a voluntary basis. The TCDC offers incentives to help develop a counterpart to the TCDC’s ALR system in hospital settings. Although including EMR data into laboratory-reportable cases can improve the specificity of ELR, few studies have evaluated such systems. In this study, we developed a counterpart to the TCDC’s ALR system for a hospital setting and evaluated its effectiveness in terms of sensitivity and specificity compared with the existing WebNDR system.


Settings

In this study, we implemented a counterpart of the TCDC’s ALR system in a teaching affiliate hospital with about 1000 beds starting in August 2014 in Taiwan. The hospital has an LIS to manage laboratory orders and results, a computerized physician order entry (CPOE) system to support clinicians in their daily clinical practice including entering test orders and reviewing test results, and other EMR systems. It can also access the WebNDR system to report notifiable diseases manually.

The Framework of Automated Laboratory Reporting System in a Hospital Setting

Based on the framework of the TCDC’s ALR system, this study documents the implementation in a hospital setting, termed the Hospital Automated Laboratory Reporting (HALR) system (Figure 1). The HALR system consists of four major modular components: HALR Gateway, Reportable Pathogens Update (RPU), Automated Detection of Reportable Pathogens (ADRP), and Notifiable Case Reporting (NCR). The HALR Gateway is responsible for downloading the latest notifiable disease pathogens from the TCDC’s ALR system and uploading reportable cases to the TCDC ALR system for further processing. Using the RPU module, the downloaded pathogens are then used to update the Notifiable Pathogen Database (NPDB) in the HALR system. This belongs to the out-of-hospital system data and is indicated by a thick line.

When the LIS releases a pathogen test result, the ADRP module checks the pathogen against the NPDB. LIS pathogen test results include patient ID, name, gender, test item, specimen type, and pathogen report content. If the pathogen can be found in the NPDB and its test result is positive, the test case is labeled as reportable. The patient’s information is then included with the pathogen test result to form a reportable case. The laboratory test is encoded with a hospital code; however, the TCDC requires a laboratory test with a Logical Observation Identifiers Names and Codes (LOINC) code.

Figure 1. The framework of a Hospital Automated Laboratory Reporting (HALR) System. (The thick “-” corresponds to the out-of-hospital system; the thin “-” corresponds to the in-hospital system). ADRP: Automated Detection of Reportable Pathogens, ALR: Automated Laboratory Reporting, CPOE: computerized physician order entry, DB: database, LOINC: Logical Observation Identifiers Names and Codes, NCR: Notifiable Case Reporting, NHI: National Health Insurance, RPU: Reportable Pathogens Update, TCDC: Taiwan’s Centers of Disease Control, VPN: virtual private network.
View this figure

Thus, the ADRP module can translate the hospital code of a pathogen test into its corresponding LOINC code by referencing a local LOINC cross-mapping database. The reportable test cases detected by the ADRP module are then stored into the Reportable Test Case Database (RTCDB).

Finally, the NCR module retrieves a reportable test case from the RTCDB and links the test data with the patient’s clinical information, such as disease name, diagnosis code, and the date when the condition was diagnosed, which are stored in the CPOE system. Then, the linked data are compiled into a laboratory-reportable case in a digital format such as XML that is defined by the TCDC. Subsequently, the HALR Gateway sends the laboratory-reportable case to the TCDC ALR system; this belongs to the in-hospital system data and is indicated by a thin line.

Mapping Between Pathogens and Diseases

When the LIS confirms a pathogen test result that meets a laboratory reporting condition, the pathogen test result and other information, such as order number, local code, specimen type, and time of result, will be automatically written into the RTCDB.

After retrieving new reporting data, the program converts the laboratory test items to LOINC codes and retrieves other patient information required by the TCDC from different hospital information systems and then submits the combined data to the hospital reporting module within the TCDC gateway in the required format.

Since the WebNDR system is dedicated to reporting communicable diseases, the five disease categories reported by this system are used as a basis of evaluating the HALR system. The TCDC provides a table for mapping between pathogens and their related diseases (Table 1).

System Evaluation

After the HALR system was implemented in a regional hospital in Taipei, we collected reported cases from the HALR and WebNDR systems for 6 months, from December 2014 to May 2015. Since the reported cases from the WebNDR system were confirmed by infectious control professionals, they served as a gold standard for evaluating the HALR system in this study. Thus, the sensitivity and specificity of the reported cases by the HALR system could be evaluated according to the following definitions:

Sensitivity (true positive rate): If a patient has a notifiable disease reported by the WebNDR system, the sensitivity is the probability that the HALR system would report the case. The numerator is the number of cases identified as positive and reported by both the HALR and WebNDR systems, whereas denominator is the total number of cases reported by the WebNDR system (# of reported cases by both the HALR and WebNDR systems)/(# of cases reported by the WebNDR system).

Specificity (true negative rate): If a patient does not have a notifiable disease and is not reported by the WebNDR system, the specificity is the probability that the HALR system would not report the case. The numerator is the number of cases in whom test results were identified as negative and were not reported by the WebNDR system, whereas the denominator is the total number of cases not reported by the WebNDR system (# of not reported cases by neither the HALR nor WebNDR system)/(# of cases not reported by the WebNDR).

Table 1. Mapping between pathogens and diseases (based on the Web-based Notifiable Disease Reporting system).
PathogenICD-9-CMaDisease
Streptococcus pneumoniae481, 482, 485, 486, 038, 041, 320Invasive pneumococcal disease
Mycobacterium tuberculosis complex010-018Tuberculosis
Influenza virus487Severe complicated influenza case
Hepatitis B virus070.20,070.21,070.30, 070.31Acute hepatitis B
Hepatitis C virus070.41Acute hepatitis C

aICD-9-CM: The International Classification of Diseases, Ninth Revision, Clinical Modification.

Table 2. The predictive specificity and sensitivity values of tests evaluated for the Hospital Automated Laboratory Reporting (HALR) system (pathogen test result) compared with those for the Web-based Notifiable Disease Reporting (WebNDR) system (reported result).
MethodReported by the WebNDR systemNot reported by the WebNDR systemTotal
Reported (positive) by the HALR systemTPa(FPb)TP+(FP)
Not reported (negative) by the HALR system(FNc)TNd(FN)+TN
TotalTP+(FN)(FP)+TN

aTP: true positive.

bFP: false positive.

cFN: false negative.

dTN: true negative.

The cases reported by the WebNDR system were patients with notifiable diseases, whereas those reported by the HALR system were patients whose pathogen test results were positive. During his or her hospital stay, a patient with the same pathogen reported more than once (ie, a repeatedly reported case) was excluded.

Furthermore, since a pathogen may cause more than one disease, the HALR system includes clinical diagnosis information such as that from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Thus, the test results were combined with ICD-9-CM data to determine whether the HALR system performance could be improved.

  • Sensitivity (with ICD): The numerator is the number of cases identified as a notifiable disease with a positive test result, which were automatically reported by the HALR and WebNDR systems. The denominator is the total number of cases reported by the WebNDR system.
  • Specificity (with ICD): The numerator is the number of cases identified as negative and, therefore, not reported by either the HALR or WebNDR system. The denominator is the total number of cases not reported by the WebNDR system (Table 2).

Pathogens Analysis

Based on the TCDC’s reportable pathogens and reporting criteria, 15 pathogens were reported to the TCDC ALR system (Table 3). Among these, 5174 patients had positive test results and were reported by the HALR system.

Simultaneously, 34 cases in five disease categories were reported by the WebNDR system (Table 4). The number of reported cases from different disease categories was pulmonary tuberculosis (25), complications from severe influenza (3) and acute viral hepatitis C (3), invasive Streptococcus pneumoniae infection (2), and acute viral hepatitis B (1).

Sensitivity and Specificity Analysis

Table 5 shows the sensitivity and specificity analysis for the HALR system. In this study, the sensitivity for the HALR system reached 100%, but the specificity varied according to the pathogens. The specificity for S. pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. In addition, the specificity for influenza and hepatitis B virus were only 79.9% and 47.1%, respectively.

Because the HALR system collects a reported case that includes not only laboratory test results but also clinical information (such as ICD-9-CM) from the TCDC ALR system, only cases with associated laboratory positive test results and an associated diagnosis code that meets the TCDC’s notifiable disease definition are reported to the TCDC ALR system (Table 6). The sensitivity and specificity of cases reported by the HALR system can be recalculated (Table 6). The sensitivity performance remains unchanged, but the specificity is greatly improved, in particular for influenza (89.2%) and hepatitis B virus (99.1%). Thus, the inclusion of clinical information in the reported data can improve the specificity performance.

Table 3. The analysis of laboratory test results during the study period.
Name of the detected pathogenTotal number of subjects (N=57,511), N (%)Pathogen test results, n (%)
Negative (n=52,337)Positive (n=5174)
Notifiable diseases (law requirements)

Salmonella spp10,201 (17.8)10,173 (19.4)28 (0.5)

Streptococcus pneumoniae9970 (17.3)9946 (19.0)24 (0.5)

Mycobacterium tuberculosis complex1622 (2.8)1542 (3.0)80 (1.5)

Influenza virus1980 (3.4)1579 (3.0)401 (7.8)

EnterovirusN/AaN/AN/A

Hepatitis B virus7917 (13.8)3725 (7.1)4192 (81)

Hepatitis C virus5124 (8.9)4986 (9.5)138 (2.7)
Nonnotifiable diseases

Streptococcus agalactiae, GBSb10,576 (18.4)10,356 (19.8)220 (4.2)

Streptococcus pyogenes9958 (17.3)9898 (18.9)60 (1.2)

Parainfluenza virusN/AN/AN/A

Respiratory syncytial virus90 (0.2)80 (0.2)10 (0.2)

Rotavirus73 (0.1)52 (0.1)21 (0.4)

Yersinia enterocoliticaN/AN/AN/A

Campylobacter sppN/AN/AN/A

Listeria monocytogenesN/AN/AN/A

aN/A: not applicable.

bGBS: Group B Streptococcus.

Table 4. The analysis of the Web-based Notifiable Disease Reporting system during the study period.
Name of the detected pathogenTotal number of reported cases, n (%)
Streptococcus pneumoniae2 (6)
Mycobacterium tuberculosis complex25 (73)
Influenza virus3 (9)
Hepatitis B virus1 (3)
Hepatitis C virus3 (9)
Table 5. The sensitivity and specificity of the Hospital Automated Laboratory Reporting (HALR) system.
Pathogen name and HALR system test resultWebNDRa systemTotalSensitivity or specificity, %


Reported, n (%)Not reported, n (%)

Streptococcus pneumoniae




Positive (reported)2 (100)22 (0.2)24100b
Negative (not reported)0 (0)9946 (99.8)994699.8c
Total2 (100)9968 (100)9970
Mycobacterium tuberculosis complex




Positive (reported)25 (100)55 (3.4)80100b
Negative (not reported)0 (0)1542 (96.6)154296.6c
Total25 (100)1597 (100)1622
Influenza virus




Positive (reported)3 (100)398 (20.1)401100b
Negative (not reported)0 (0)1579 (79.9)157979.9c
Total3 (100)1977 (100)1980
Hepatitis B virus




Positive (reported)1 (100)4191 (52.9)4192100b
Negative (not reported)0 (0)3725 (47.1)372547.1c
Total1 (100)7916 (100)7917
Hepatitis C virus




Positive (reported)3 (100)135 (2.6)138100b
Negative (not reported)0 (0)4986 (97.4)498697.4c
Total3 (100)5121 (100)5124

aWebNDR: Web-based Notifiable Disease Reporting.

bRefers to sensitivity.

cRefers to specificity.

Table 6. The analysis of sensitivity and specificity between the Hospital Automated Laboratory Reporting (HALR) and Web-based Notifiable Disease Reporting (WebNDR) systems with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes.
Pathogens name (ICD-9-CM code) and resultWebNDR systemTotalSensitivity or specificity (%)
Reported, n (%)Not reported, n (%)
Streptococcus pneumoniae (ICD-9-CM: 485, 486)



Positive (reported)2 (100)12 (0.1)14100a
Negative (not reported)0 (0)9956 (99.9)995699.9b
Total2 (100)9968 (100)9970
Mycobacterium tuberculosis complex (ICD-9-CM: 011.00, 011.90)


Positive (reported)25 (100)12 (0.8)37100a
Negative (not reported)0 (0)1585 (99.2)158599.2b
Total25 (100)1597 (100)1622
Influenza virus (ICD-9-CM: 487.1)




Positive (reported)3 (100)214 (10.8)217100a
Negative (not reported)0 (0)1763 (89.2)176389.2b
Total3 (100)1977 (100)1980
Hepatitis B virus (ICD-9-CM: 070.30)




Positive (reported)1 (100)74 (0.9)75100a
Negative (not reported)0 (0)7842 (99.1)784299.1b
Total1 (100)7916 (100)7917
Hepatitis C virus (ICD-9-CM: 070.41, 070.51)




Positive (reported)3 (100)9 (0.2)12100a
Negative (not reported)0 (0)5112 (99.8)511299.8b
Total3 (100)5121 (100)5124

aRefers to sensitivity.

bRefers to specificity.


Principal Findings

In this study, we developed an HALR system for the automatic reporting of pathogen test results and clinical information. Once laboratory test results are released, the HALR system can automatically detect pathogens that meet notifiable conditions as defined by the TCDC and report the cases to the TCDC ALR system. Since the patients’ laboratory test results are usually released to their physicians far in advance of the physicians’ final diagnoses of any notifiable diseases, the HALR system can improve the timeliness for notifiable disease surveillance and control. Moreover, this system’s effectiveness is also improved as long as the doctor has included a working diagnosis.

As indicated in previous studies [7-10], an ELR is usually nonspecific. The analysis reported in this study showed that if reported data only included the results of laboratory tests, the specificity of the reported cases by the HALR system for some diseases would be quite low. This could lead to an increase in reported cases as well as increase in workload related to the investigation of suggestive cases. However, if reported data were slightly augmented with clinical information, such as the clinical diagnosis code, the specificity of the reported cases could be greatly improved. The primary reason is that a given pathogen may cause several different diseases, which are not notifiable. For example, a hepatitis B virus infection may lead to acute or chronic hepatitis, but only acute hepatitis B (with ICD-9-CM diagnosis code 070.30) must be reported. Similarly, influenza viruses may cause acute respiratory infections, from mild to severe, but only cases with severe complications from influenza (ICD-9-CM 487.1) must be reported. However, because influenza is caused by a variety of pathogens and easily causes pandemics, the National Respiratory and Enteric Virus Surveillance System of the World Health Organization provides influenza-like illnesses with clear definitions and clinical symptom identification standards, which must be updated and revised according to emerging viruses and changes in patients’ disease features. The HALR system reports not only laboratory test results but also relevant clinical information and, thus, can greatly improve its performance.

In response to emerging infectious diseases, the HALR system detects reportable cases based on the NPDB. This design allows the TCDC to add, delete, or update the definitions of reportable pathogens as new emerging threats are identified. Hence, as this system can routinely download and update the NPDB, it can quickly respond to altered requirements.

The improvement of the sensitivity and specificity of reportable cases depends not only on timely and accurate laboratory test results but also on the availability of clinical information required to identify cases. This study simultaneously examined reporting data on the basis of test results and clinical evidence in order to enable data sharing between information systems. Through double verification, with this approach, hospitals can immediately confirm patients’ conditions and submit reports when required. Thus, the sensitivity and specificity for early disease detection can be improved. The findings of this study can serve as a reference for disease prevention measures in clinical care.

Limitations

The following are some limitations of this system. First, the system will be limited to tests accepted by patients in the hospital. This system can only automatically link information when required clinical data are complete. Second, the notification condition of the test and the positive definition of the pathogen remain important factors affecting the accuracy of the content of the notification.

Conclusions and Future Directions

In this study, we developed an HALR system in a hospital to automatically and actively report pathogen test results and relevant clinical information to the TCDC ALR system. The study results show that the HALR system can improve the timeliness, sensitivity, and specificity of reported cases. Furthermore, it can provide the flexibility to integrate frequent changes to the definitions of notifiable cases when the TCDC finds new, emerging threats or diseases.

The improvement of the sensitivity and specificity of reportable cases depends not only on timely and accurate laboratory test results but also on the availability of clinical information that may be required for identified cases. This study recommends the following feasible solutions: integrate automatic pathogen reporting by the HALR system with related medical data in patients’ EMRs through the automatic system determination, with EMR data provided according to the TCDC clinical standards specifications for infectious diseases (eg, according to a “Dengue Fever” diagnosis, clinical data can include a patient’s fever [>38°C] and at least one of the following symptoms: retro-orbital pain, myalgia, arthralgia, rash, leukopenia, and hemorrhagic manifestations.)

Conflicts of Interest

None declared.

References

  1. Vogt RL. Laboratory reporting and disease surveillance. J Public Health Manag Pract 1996;2(4):28-30. [Medline]
  2. Wurtz R, Cameron BJ. Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. Clin Infect Dis 2005 Jun 01;40(11):1638-1643. [CrossRef] [Medline]
  3. Overhage JM, Grannis S, McDonald CJ. A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. Am J Public Health 2008 Feb;98(2):344-350. [CrossRef] [Medline]
  4. Backer HD, Bissell SR, Vugia DJ. Disease reporting from an automated laboratory-based reporting system to a state health department via local county health departments. Public Health Rep 2001;116(3):257-265 [FREE Full text] [CrossRef] [Medline]
  5. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010 Aug 05;363(6):501-504. [CrossRef] [Medline]
  6. Centers for Medicare & Medicaid Services. US Government Publishing Office. Medicare and Medicaid Programs; Electronic Health Record Incentive Program   URL: https://www.gpo.gov/fdsys/pkg/FR-2012-09-04/pdf/2012-21050.pdf [accessed 2018-06-26] [WebCite Cache]
  7. Panackal AA, M'ikanatha NM, Tsui F, McMahon J, Wagner MM, Dixon BW, et al. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis 2002 Jul;8(7):685-691 [FREE Full text] [CrossRef] [Medline]
  8. M'ikantha NM, Southwell B, Lautenbach E. Automated laboratory reporting of infectious diseases in a climate of bioterrorism. Emerg Infect Dis 2003 Sep;9(9):1053-1057 [FREE Full text] [CrossRef] [Medline]
  9. Klompas M, Platt R, Hou X, Campion FX, Kruskal B, Haney G, et al. MMWR Weekly. 2008 Apr 11. Automated Detection and Reporting of Notifiable Diseases Using Electronic Medical Records Versus Passive Surveillance—Massachusetts, June 2006-July 2007   URL: https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5714a4.htm [accessed 2018-06-26] [WebCite Cache]
  10. Allen-Dicker J, Klompas M. Comparison of electronic laboratory reports, administrative claims, and electronic health record data for acute viral hepatitis surveillance. J Public Health Manag Pract 2012;18(3):209-214. [CrossRef] [Medline]
  11. McHugh LA, Semple S, Sorhage FE, Tan CG, Langer AJ. MMWR Weekly. 2008 Jan 8. Effect of Electronic Laboratory Reporting on the Burden of Lyme Disease Surveillance --- New Jersey, 2001--2006   URL: https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5702a4.htm [accessed 2018-06-26] [WebCite Cache]
  12. Shiffman RA, Brandt CA, Liaw Y, Corb GJ. A design model for computer-based guideline implementation based on information management services. J Am Med Inform Assoc 1999;6(2):99-103 [FREE Full text] [Medline]
  13. Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005;24(5):1103-1117. [CrossRef] [Medline]
  14. Simborg DW. Promoting electronic health record adoption. Is it the correct focus? J Am Med Inform Assoc 2008 Mar;15(2):127-129 [FREE Full text] [CrossRef] [Medline]
  15. O'Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE. Impact of an electronic medical record on diabetes quality of care. Ann Fam Med 2005 Jul;3(4):300-306 [FREE Full text] [CrossRef] [Medline]
  16. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008 May;77(5):291-304. [CrossRef] [Medline]
  17. Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ, Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control 2008 Apr;36(3 Suppl):S37-S46. [CrossRef] [Medline]
  18. Wright MO, Fisher A, John M, Reynolds K, Peterson LR, Robicsek A. The electronic medical record as a tool for infection surveillance: successful automation of device-days. Am J Infect Control 2009 Jun;37(5):364-370. [CrossRef] [Medline]
  19. Pokorny L, Rovira A, Martín-Baranera M, Gimeno C, Alonso-Tarrés C, Vilarasau J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2006 May;27(5):500-503. [CrossRef] [Medline]
  20. Lo Y, Lee W, Chen G, Liu C. Improving the work efficiency of healthcare-associated infection surveillance using electronic medical records. Comput Methods Programs Biomed 2014 Nov;117(2):351-359. [CrossRef] [Medline]
  21. Calderwood MS, Platt R, Hou X, Malenfant J, Haney G, Kruskal B, et al. Real-time surveillance for tuberculosis using electronic health record data from an ambulatory practice in eastern Massachusetts. Public Health Rep 2010;125(6):843-850 [FREE Full text] [CrossRef] [Medline]
  22. Samoff E, Fangman MT, Fleischauer AT, Waller AE, Macdonald PDM. Improvements in timeliness resulting from implementation of electronic laboratory reporting and an electronic disease surveillance system. Public Health Rep 2013;128(5):393-398 [FREE Full text] [CrossRef] [Medline]
  23. Laws & Regulations Database of The Republic of China. 2016 Jul 07. Regulations Governing the Implementation of the Epidemiological Surveillance and Advance-Alert System for Communicable Disease   URL: http://law.moj.gov.tw/Eng/LawClass/LawContent.aspx?PCODE=L0050006 [accessed 2018-06-26] [WebCite Cache]
  24. Yuping H, Jialin L, Lun L, Qiming Z, Xiang Z, Jen-Hsiang C. Reporting Notifiable Infectious Diseases by Using Hospital Electronic Medical Record Systems: An Effectiveness Assessment. The Journal of Taiwan Association for Medical Informatics 2016;25(1):23-31 [FREE Full text]
  25. Lo H, Yang S, Chou P, Chuang J, Chiang C. Completeness and timeliness of tuberculosis notification in Taiwan. BMC Public Health 2011 Dec 12;11:915 [FREE Full text] [CrossRef] [Medline]
  26. Taiwan's CODC. Operation Manual for the Automatic Laboratory Reporting System and a Platform for Laboratory Information Exchange across Hospitals (V3.1) (in Chinese)   URL: http:/​/www.​cdc.gov.tw/​professional/​info.​aspx?treeid=4c19a0252bbef869&nowtreeid=1a8c498af5f8af5d&tid=763A28C245DF7CC6


ADRP: Automated Detection of Reportable Pathogens
ALR: Automated Laboratory Reporting
CPOE: computerized physician order entry
ELR: electronic laboratory reporting
EMR: electronic medical record
FN: false negative
FP: false positive
GBS: Group B Streptococcus
HALR: Hospital Infectious Disease Laboratory Autoreporting
ICD-9-CM: The International Classification of Diseases, Ninth Revision, Clinical Modification
LIS: Laboratory Information System
LOINC: Logical Observation Identifiers Names and Codes
NCR: Notifiable Case Reporting
NPDB: Notifiable Pathogen Database
RPU: Reportable Pathogens Update
RTCDB: Reportable Test Case Database
TCDC: Taiwan’s Centers of Disease Control
TN: true negative
TP: true positive
WebNDR: Web-based Notifiable Disease Reporting


Edited by G Eysenbach; submitted 02.05.18; peer-reviewed by LC Yu, IC Lin, MIK Almani, C Fincham; comments to author 31.05.18; revised version received 04.06.18; accepted 19.06.18; published 08.08.18

Copyright

©Mei-Hua Wang, Han-Kun Chen, Min-Huei Hsu, Hui-Chi Wang, Yu-Ting Yeh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.08.2018.

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