Biosurveillance and Disaster Reporting: Barriers to Accuracy and Timeliness

Biosurveillance and Disaster 1

Running Head: BIOSURVEILLANCE AND DISASTER

Biosurveillance and Disaster Reporting: Barriers to Accuracy and Timeliness

Nia Llenas

UMUC

Peer Reviewer: Carl Mueller


Executive Summary

This report details the purpose of biosurveillance and disease reporting systems

and the barriers to early adoption and success. Much like the federal government’s

BioSense project, state and local health departments are rapidly deploying their own

disease surveillance systems, in an effort to connect the dots between local and regional

health care providers.

Unfortunately, implementing and maintaining a surveillance system requires skill

and attention that many health professionals have yet to practice. While previous studies

have shown success in timeliness and completeness of data capture and transmission,

much of the workflow is left to human intervention.

Several areas to be considered when implementing disease surveillance or disaster

response systems are patient data privacy/security, system languages and coding, and

staff training. Dually noted, are the limitations to current research, namely the small

sample sizes in previous work and the lack of accuracy in electronic laboratory reporting.

In considering biosurveillance system implementation, health care administration

must look to secure patient data behind facility firewalls. Additionally, ensuring that

information is contained in HL-7 fields, coded in LOINC, SNOMED, IDC-9 and NDC,

minimizes delays in downloading and translation by public health officials. Finally, staff

should be trained diligently to recognize the importance of reporting notifiable diseases,

completely and in a time-efficient manner.


Table of Contents

1. Introduction

a. Purpose

b. Problem

c. Background

d. Objectives

2. Literature Review

a. Implementation

b. ELR and EMR reporting

3. Barriers to adoption

a. Patient data security

b. Languages

c. Training staff

d. Reporting and monitoring limitations

4. Conclusion

5. References

6. Appendices

7. Glossary


Introduction

In 2007, the Department of Health and Human Services released a working draft

of the Biosurveillance Workgroup Priority Areas. The stated focus of the workgroup is to

“recognize the critical importance of an integrated and interconnected public health and

healthcare delivery system that enables real-time, secure, and appropriate bi-directional

exchange of information” (Biosurveillance Priority Areas, 2007). Specifically, the

workgroup centers around the effort to produce a secure and timely method of

transmitting standardized data from a patient’s electronic medical records (EMR) and

electronic laboratory systems (ELR) to public health officials and back. This direct

communication between public health systems, healthcare delivery organizations, VA,

laboratory organizations and poison control centers will result in real-time reporting of

adverse events and outbreaks, while providing current public health statistics to

clinicians.

Several factors play a role in the success of systems like BioSense; real-time

transmission, geospatial mapping, patient privacy, and concise ICD-9 coding are some of

the areas under examination that can affect the reliability of surveillance systems. In this

paper, the barriers to timely, accurate surveillance data are investigated in conjunction

with the efforts of several states to implement effective surveillance and disaster response

systems.

In the past, communicable disease outbreaks and regional health statistics have

been largely reported on paper-based systems. Early adoption of electronic laboratory

reporting systems have subsequently reduced reporting errors while increasing test

volume, thereby being hailed as milestone in disease surveillance (Nguyen, Lorna,


Mostashari, & Makki, 2007). Unfortunately, problems still arise and early adoption has

been slow as public health officials determine vocabulary standards of laboratory code,

patient identification/security and HIPPA considerations (Overhage, Grannis, &

McDnoald, 2008)

Currently, public health is still reliant on clinicians to interpret laboratory data and

assign specific demographic characteristics to that data in order to transmit useful

information to public health officials. This process is time consuming as most of it is still

done by fax, email or phone, resulting in incomplete data capture (Klompas, et al., 2007).

Overhage, Grannis and McDonald (2008) recently reported that in a study of the Vermont

Department of Health, 71% of all infectious cases were reported directly by laboratories,

giving more meaning to the need for automated ELR and EMR systems link to public

health officials.

This present research attempts to address the problems associated with the

implementation of detection systems, data collection and data transmission to the national

disease surveillance system and subsequent effects of disaster response on the health

system. This directly relates to the concepts under review in the University of Maryland

University College HCAD 610 course.

Literature Review

Implementation

Previous research provides similar experiences with structuring and implementing

surveillance systems. Brownstein and Mandl (2006) reported the efforts of the

Massachusetts Department of Public Health transforming its AEGIS system into a real-

time influenza monitoring system using chief complaint data from emergency


departments to formulate the predictions of the start of influenza and pneumonia

outbreaks, subsequently comparing data to trends of the previous five years. Although

AEGIS was able to accurately predict influenza outbreaks, some systems are struggling

to transmit consistently accurate reports.

Grannis, Wade, Gibson and Overhage (2006) presented early finding from the

Indiana State Department of Health’s statewide surveillance project. The platform is built

on a standard of HL7 messages containing chief complaint data, clinical messaging and

ELR, transmitting results from emergency departments to local and state health

departments for review. Public health officials are attempting to reduce the number of

false positive signals that prompt human interaction and review, furthermore causing

unnecessary staff attention and investigation.

On a larger scale, clinical systems are rapidly testing and implementing

surveillance systems to include multi-location hospitals and outpatient facilities.

Gundlapalli et al., (2007) detailed a pivotal study on the implementation in of hospital

EMR surveillance during the 2002 Winter Olympic Games in, in which, a rules-based

system was developed to monitor admission/discharge/transfer, laboratory and radiology

databases of Utah’s University Hospital system. Klompas et al. (2007) also undertook

multi-practice implementation, which succeeded in protecting patient privacy in data

transfers to public health authorities by keeping patient health information (PHI) behind

the facilities firewall.

ELR and EMR reporting

Laboratories and hospital records play a pivotal role in disease surveillance.

While labs cannot attest to the nature of patient care, they do however possess the


“information infrastructure and processes to facilitate reporting” (Overhage, Grannis, &

McDnoald, 2008). Unfortunately, the transition from paper to electronic lab reporting has

been slow, according to Nguyen et al. (2007) report on the New York City Department of

Mental Health and Hygiene (NYC DOMHM) largely due to improper training and low

sense of urgency among laboratory staff. Additionally, Overhage et al. (2008), et al found

that lack of communication with laboratory IT staff greatly hampered efforts to transition

to electronic reporting methods.

The use of EHR in surveillance and disaster response has been mastered by the

Department of Veterans Affairs (VA) medical system. During the most recent disaster,

Hurricane Katrina, the VA “rehosted” its VistA system by moving all data to Houston. In

effect, they lost only four days of access and were able to create a web-based system for

access to patient files of those who were displaced (Brown, et al., 2007). As an

afterthought, patient data of those who survived the disaster provides real-time

biosurveillance information to both the VA and Department of Defense.

Data security and privacy

Data security is a concern not only for patients but medical and public health

officials alike. When clinics and laboratories transmit data to health departments, the

CDC and the federal BioSense surveillance systems, data security parameters vary

widely. The Harvard Vanguard’s Medical Associates’ Electronic medical records Support

for Public health (ESP) database and software systems are located separately on location,

not only to relieve any burden on the host server, but to also secure PHI behind the

practice firewall until information is sent to the health department (Klompas, et al.,

2007). Similarly, the TheraDoc system, utilized during the 2002 Winter Olympics,


available for access through the hospital intranets, was subject to the same security layers

as the hospital itself. In this instance, only infection control practitioners were provided

access to surveillance data. (Gundlapalli et al., 2007)

The aforementioned models serve as examples of measures taken to properly

secure access and transmission of PHI. Unfortunately, there are issues with securing all

demographic attributes of a patient diagnosed with a notifiable condition and

subsequently compiling accurate data. BioSense uses a complex set of rules to define

geographic location of infection. By utilizing the reporting facilities zip code and the

patient residence zip code, a common zip code is mapped and recorded as the point of

infection (English, et al., 2006). Likewise, cluster detection is at the forefront of the

patient privacy debate in surveillance. Epidemiologists are arguing whether the insecurity

of using patient locations (home addresses) to pinpoint disease clusters outweighs the risk

of less accurate data associated with using administrative regions (zip codes). Studies

have shown that exact coordinates found 73% of disease clusters compared to 45% of

administrative coordinates, and are likely to lead other surveillance systems to use the

same information, leaving patients health records exposed. (Olson, Grannis, & Mandl,

2006)

Languages

Currently, there are several major languages utilized by disease surveillance

systems. HL7 is the nationally recognized standard for communication between hospitals,

laboratories and public health applications and is frequently used in conjunction with

free-text messaging in the transmission of patient information (Grannis, Wade, Gibson, &

Overhage, 2006). Equally important is the HL7 supported bi-directional communication


in free-text, supporting quick responses to health department queries for case details from

laboratories and clinic sites (Klompas, et al., 2007). The importance of HL7 in disease

surveillance will become more apparent as more clinic level providers begin using EMR

systems, supplying more in-depth patient information to surveillance systems.

Surveillance systems also read SNOMED and LOINC codes, the universal

medical and laboratory terminology used by clinicians, practices, laboratories and

hospitals. Laboratories have historically used free-text coding instead of SNOMED,

causing some issues in workflow for public health officials. Ideally, laboratory tests

should be identified using LOINC, there results as SNOMED and finally the diagnoses as

ICD-9. The NYC DOHMH saw a high numbers of false positives due to laboratory staff

forsaking the use of proper codes for free-text, causing common illnesses to flag more

serious rare illnesses (Nguyen, Lorna, Mostashari, & Makki, 2007).

Training staff

It has been suggested that the success of disease surveillance largely depends on

the level of trust, respect and familiarity between health officials and healthcare

personnel (Buehler, Isakov, Prietula, Smith, & Whitney, 2007). When introducing new

systems and workflows to hospital, laboratory and IT staff, careful measures must be

taken to stress the importance of compliance. Staff must be trained to use and trouble

shoot the system effectively and efficiently. In actuality, implementing an ELR system

that reports to state health systems may require site certification that depends on the staff

knowledge and presence.

Laboratories in New York City must complete a certification process to transmit

electronically to public health departments and additionally, lab staff and IT department’s


need training on quality assurance and database management (Nguyen, Lorna,

Mostashari, & Makki, 2007). Success in reporting completeness has also been attributed

to provider awareness, staff motivation, and effective processes in laboratories and clinics

where systems are monitored regularly (Overhage, Grannis, & McDnoald, 2008).

Therefore, staff should undergo training to stress the importance of proper coding and

reporting and its implication on local and national efforts to maintian healthy

communities.

Reporting limitations

While electronic surveillance is an effective way to monitor communicable or

notifiable diseases, it does carry risks that are assumed as a result of reporting limitations.

Current research is generally limited to local databases and laboratories, therefore

limiting sample sizes. Equally important, many notifiable conditions do not translate well

in ELR, such as tuberculosis, syphillis and meningitis. Condidtions requiring multiple or

varied tests to confirm a true positive are frequently reported incorrectly and may prompt

further investigation from public health officials (Nguyen, Lorna, Mostashari, & Makki,

2007) (Overhage, Grannis, & McDnoald, 2008).

Recommendations

A strong push towards electronic methods of storing and transmitting medical

data will transform the biosurveillance industry and health care providers must prepare

themselves with the knowledge and facilities to join in. Transmitting patient and disease

data will no doubt require careful planning regarding vendor systems, integration into the

state and local networks, designating staff, improving training and mastering quality

assurance.


Also, being aware that as technology evolves, barriers to success are consistently

present. These barriers mentioned in this paper, can be overcome through new , thorough

innovations in electronic laboratory reporting and expanded research in the field.

Conclusion

As the U.S. moves towards a more complete and connected biosurveillance

system, consideration must be given to the security, timeliness and accuracy of the data

and its transmission. All efforts to respect patient privacy should be taken, even if at the

expense of accuracy. Furthermore, in order for system implementation and execution to

be successful, laboratory teams and clinical staff must have proper training that highlights

the importance of data reporting to local health departments and the CDC.

Biosurveillance and disaster reporting depend on accuracy and dedication of all involved,

for without it, national security and health may be at risk.


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Appendix

View of patient distribution map from BioSense

Downloaded from http://www.cdc.gov/biosense/images/patient_distribution_full.jpg

BioSense map of weekly volume of notifiable diseases from LabCorp

Downloaded from http://www.cdc.gov/biosense/publichealth.htm

http://www.cdc.gov/biosense/publichealth.htm

http://www.cdc.gov/biosense/images/patient_distribution_full.jpg


Clinic system formats data:Data fields>HL7Lab tests>LOINCResults>SNOMEDDiagnoses>ICD-9Prescription>NDC

Case is identified; system is queried

ICD-9 codes and vital are assessed to determine if case is reportable disease

Case is sent to health department and CDC for submission to local and national database

Yes

Case management system presents infection control practitioner (ICP) with list of suspected positives

Possible positives

ICP reviews cases behind practice firewall

ICP rejects false positives

ICP sends positives back to ordering clinician, and reports to health department and CDC

Flow of data from reporting health system to the surveillance system, i.e. Health department, BioSense, etc.


Glossary

BioSense- national real-time surveillance system operated by the Centers for Disease

Control. Information can be found at http://www.cdc.gov/biosense/

Biosurveillance- related to epidemiology; immediate monitoring of public health data

preferable through electronic reporting

Deidentification- terminology used to describe the process systems use to encrypt patient

data in order to comply with HIPPA regulations

HL7-Health Level 7 information can be found at http://www.hl7.org/

ICD-9- International Classification of Diseases information can be found at

http://www.cdc.gov/nchs/icd9.htm

NDC- National Drug Code directory information can be found at

http://www.fda.gov/cder/ndc/

Notifiable diseases- diseases that present a danger to the public and should be

immediately reported to public health officials

LOINC-Logical Observation Identifiers Names and Codes information can be found at

http://www.regenstrief.org/medinformatics/loinc/

SNOMED or SNOMED-CT- Systemized Nomenclature of Medicine- Clinical Terms

information can be found at http://www.snomed.org/

http://www.snomed.org/

http://www.regenstrief.org/medinformatics/loinc/

http://www.fda.gov/cder/ndc/

http://www.cdc.gov/nchs/icd9.htm

http://www.hl7.org/

http://www.cdc.gov/biosense/

Documents

Biosurveillance and Disaster Reporting: Barriers to Accuracy and Timeliness