Mass Fatality Incident Data Management: Best Practice Recommendations for the 

Medicolegal Authority

Mass Fatality Incident 

Data Management: Best 

Practice Recommendations 

for the Medicolegal 

Authority

Disaster Victim Identification Subcommittee

Crime Scene/Death Investigation Scientific Area Committee

Organization of Scientific Area Committees (OSAC) for Forensic Science
Mass Fatality Incident Data Management: Best Practice Recommendations for the 

Medicolegal Authority

OSAC Proposed BPR

Mass Fatality Incident Data 

Management: Best Practice 

Recommendations for the Medicolegal 

Authority

Prepared by

Disaster Victim Identification Subcommittee

Version: 1.0

May 2019

Disclaimer:

This document has been developed by the Disaster Victim Identification Subcommittee of the 

Organization of Scientific Area Committees (OSAC) for Forensic Science through a consensus 

process and is proposed for further development through a Standard Developing Organization 

(SDO). This document is being made available so that the forensic science community and 

interested parties can consider the recommendations of the OSAC pertaining to applicable 

forensic science practices. The document was developed with input from experts in a broad array 

of forensic science disciplines as well as scientific research, measurement science, statistics, law, 

and policy. 

This document has not been published by a SDO. Its contents are subject to change during the 

standards development process. All interested groups or individuals are strongly encouraged to 

submit comments on this proposed document during the open comment period administered by 

the Academy Standards Board (www.asbstandardsboard.org). 
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Mass Fatality Incident Data Management: Best Practice Recommendations for the 

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Mass Fatality Incident Data Management:

Best Practice Recommendations for the Medicolegal Authority

1 Foreword

DVI is a complicated process that necessitates the management of multiple layers of data. 

Regardless of the DVI data format (i.e. paper versus digital) and incident scale and complexity, there 

are overarching data management principles that dictate appropriate and effective management of 

data. These general principles are outlined in this document. Management of digital data introduces 

challenges associated with data compatibility, accuracy, reliability, and exchange that do not exist 

with non-digital records. The best practices presented in this document pertain to the management 

of digital DVI data.

These best practices are put forth by the Disaster Victim Identification subcommittee within OSAC. 

This document originated from the Scientific Working Group on Disaster Victim Identification 

(SWGDVI).

2 Acknowledgements

Editor: Jason Wiersema, Harris County Institute of Forensic Sciences 

Drafting Working Group Members:

Kenneth W. Aschheim, New York City - Office of Chief Medical Examiner 

Donald Bloom, Disaster Mortuary Operational Response Team

Franklin Damann, National Museum of Health and Medicine 

Frank DePaolo, New York City - Office of Chief Medical Examiner 

Shuala Drawdy, International Committee of the Red Cross

Elias Kontanis, National Transportation Safety Board

Rene Pape, Plass Data

Amanda Sozer, SNA International

Sharon Stanford, American Dental Association

Naeem Ullah, New York City - Office of Chief Medical Examiner 

Brad Wing, National Institute of Standards and Technology 

Allison Woody, Harris County Institute of Forensic Sciences
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3 Table of Contents

1 Foreword................................................................................................................................................................ 1

2 Acknowledgements............................................................................................................................................1

3 Table of Contents ................................................................................................................................................1

4 Scope ........................................................................................................................................................................ 1

5 Terms and Definitions...................................................................................................................................... 2

6 Recommendations............................................................................................................................................. 5

6.1 Data Management System Components .................................................................................................... 6

6.2 DVI-Relevant Data Exchange Standards................................................................................................... 10

6.3 Adherence to Existing Data Exchange Standards/Guidance .............................................................11

7 Tables...................................................................................................................................................................... 12

Annex A (informative) Foundational Principle........................................................................................................ 17

Annex B (informative) Bibliography.............................................................................................................................18
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4 Scope

This document identifies the individual components of effective DVI data management systems, and 

reconciles them with the most appropriate applicable, non-fatality management specific data 

management standards. The components identified in this document are best practice 

recommendations regarding the capabilities that a data management strategy should include given 

appropriate resources. DVI practitioners should adhere to the best practices identified in this 

document to the extent possible, practical, and appropriate. In the absence of specific guidelines for 

particular data types or methods of data exchange, storage, or protection, the principle, spirit, and 

intent of these guidelines should be met. Although the principles of data management are similar, a

distinction should be made between the approach to data management for identifications made 

during normal daily medicolegal operations and the data management approach following a mass 

fatality incident. While the types of data that are managed are similar, the approach in recording 

and managing the data is different. Case management systems used in daily operations are 

primarily a repository for decedent data, whereas DVI data management systems are more 

involved as they also facilitate large scale and often evolving data comparisons in the interest 

of identification. While the general principals apply to all aspects of the data management strategy, 

the best practices described below apply to DVI data management information systems.

5 Terms and Definitions

Data management involves the systematic collection, organization, validation (including quality 

assurance and control), analysis, interpretation, protection, reporting and storing of data, to ensure 

that the data are reliable, accurate, and of high quality. The primary goal of DVI data management is 

to facilitate the efficient utilization of antemortem data, scene and recovery data, postmortem data, 

and contextual information to identify the victims of a mass fatality incident. The following is a list of 

data management considerations that are relevant to the DVI process (2):

Data collection 

Data ownership

Data security/confidentiality/protection 

Data storage/retention

Data protection

Data verification/validation 

Data compatibility

Data centralization/analysis 

Data reporting

Data exchange

Each principle and its applicability to DVI data management operations are described below.

5.1

Data Collection

The strategy for the acquisition of data should involve protocols to ensure the integrity, reliability, and validity 

of the collected data. The local medicolegal authority should also develop a standard data format and collection 

protocol to maximize the efficient and effective use of data. These protocols will not involve the scientific content 

of DVI data but the data collection process and record format. These protocols should outline what data is 

collected and how it is collected and recorded. Data collection procedures should facilitate: later data 

reconstruction, reproduction of results, and the systematic evaluation of data reliability, integrity, and validity. 

It is beyond the scope of this document to address these issues, but this document will provide guidance 

specific to DVI data collection. The INTERPOL Disaster Victim Identification Guide (3) and theNational
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 Institute of Justice “Mass Fatality Incidents: A Guide for Human Forensic Identification” (4) provide

additional recommendations for mass fatality management and human remains identification.

5.2

Data Ownership

The appropriate repository and ownership for DVI data of all types must be determined in advance 

of a MFI response. Relevant questions include: who maintains legal rights to DVI data; who retains 

this data after a MFI response; and, who is provided access to the data and via what transmission 

protocols? DVI data are typically the responsibility of the local jurisdictions within which it is 

collected, and the responsible parties need to develop standard operating procedures to address 

data ownership.

5.3

Data Security/Confidentiality/Protection

Ensuring the security and confidentiality of sensitive antemortem and postmortem data collected 

during the DVI operations is critical to the integrity of the process. Appropriate measures should be 

taken to protect personal data collected and exchanged during a DVI response1. There are 

numerous sources of guidance for the protection of DVIdata including the following documents:

International Committee of the Red Cross (ICRC) Report: The Missing and Their 

Families Section 26, 2003 (5)

ICRC Report: Missing People, DNA Analysis, and Identification of Human Remains: A 

Guide to Best Practice in Armed Conflicts and Other Situations of Armed Violence, 

Second Edition 2009 (6)

The United Nations Guidelines Concerning Computerized Personal Data Files, 1990 (7) 

The Organization for Economic Cooperation and Development Guidelines Governing the 

Protection of Privacy and Transborder Flows of Personal Data, 1980 (8)

The Council of Europe Convention for the Protection of Individuals with regard to 

Automatic Processing of Personal Data, 1981 (9)

The guidance in these documents can be distilled to the following criteria. It is a best practice 

recommendation that the following criteria be included in the development and deployment of DVI 

data management solutions and systems:

"Personal data" means any information relating to an identified or identifiable 

individual, including documents collected or copied in view of contributing to the 

process of identification.

“Sensitive data" means data likely to give rise to unlawful or arbitrary discrimination 

based on racial or ethnic origin, nationality, religious or other beliefs, sexual behavior, 

criminal prosecutions and convictions, medical data or health information, including 

antemortem data, postmortem data, or DNA profiles.

Personal and sensitive data shall be collected and processed fairly and lawfully, with 

appropriate safeguards.

1 The Health Insurance Portability and Accountability Act (HIPAA) restricts the disclosure of patient information and is not suspended due to an MFI. The Privacy 

Rule: Disclosures for Public Health Activities (45CFR 164.512(b)) (12) allows providers to share information (e.g., location, general condition or notification of death) 

during emergencies in order to identify, locate, and notify family members, guardians, or anyone else responsible for the individual’s care. Providers are 

alsopermitted toshare information to disasterrelief organizations without obtaining the patient’s permission ifnot doing so would interfere withthe organization’s 

ability to respond to the emergency. The disaster relief organizations are not covered by the HIPAA Privacy Rule and can therefore share patient information, if 

necessary.
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The collection and processing of personal and sensitive data shall be limited to that 

which is necessary for the purpose identified at the time of collection, or beforehand.

Personal data should be accurate, complete, and updated as necessary for the purpose for 

which they are used.

Personal and sensitive data may not be used, disclosed, or transferred for purposes 

other than those for which they were collected without the consent of the person 

concerned, except if required by a substantial public interest or for the protection of the 

vital interests of the person concerned or of others.

Personal and sensitive data may only be transferred to third parties (as defined in the 

above listed documents) respecting personal data protection principles.

The de-identification of personal and sensitive data should be considered as soon as the 

purpose of their collection has been fulfilled. However, consideration should be given to 

a long term secured archiving of the data if it may be required for the benefit of 

the

individuals, any unidentified victims, DVI organization handling the data, or if it may be 

essential or benefit the future performance of the relevant and appropriate tasks of the 

organization which collected the data.

Access to personal and sensitive data should be granted only to those individuals 

to whom the data relates. Provision should also be made for the right to challenge 

the

accuracy and completeness of the data and to have them amended as appropriate.

5.4

Data Storage/Retention

A comprehensive data storage strategy is a critical component of DVI operations. Poor 

communication and lack of robust data sharing policies and procedures can often result in duplicate 

data as well as data silos that complicate the DVI process. Pertinent questions are: what is the 

appropriate amount of data to be stored to facilitate the appropriate reconstruction of data after 

the incident, and the efficient use of that data for comparison for identification. It is also important to 

maintain a strategy for length of time the data will be stored, and for the ultimate disposition and 

possible destruction of the data.

5.5

Data Protection

Protection of DVI data is critical to the integrity of any DVI operation. Medicolegal jurisdictions 

should maintain protocols to ensure data protection (in accordance with pertinent legal statutes) 

during data collection, analysis, exchange, storage, and release processes. These protocols should 

ensure that the data that will become public record should, when practical and possible, be 

communicated first to the decedent’s next of kin, and that non-public records are securely 

maintained in accordance with a data storage/retention strategy.

5.6

Data Verification/Validation

The ability to make scientifically reliable identifications is dependent on the reliability of the data that 

is collected and maintained. Whenever possible, quality reviews should be performed to assess the 

accuracy and completeness of the data. If issues exist, they need to be addressed in order to 

prevent unrecognized erroneous data from having detrimental effects later in the process. Data 

verification is a systematic process for evaluating the performance and conformance of a set of data 

when compared to a set of standards to ascertain the data’s completeness, correctness, and 

consistency using the methods and criteria defined in the project documentation. Data validation 

follows the data verification process and uses information from the project documentation to 
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ascertain the usability of the data in light of its measurement quality objectives and to ensure that 

results obtained are scientifically defensible. Data verification and validation is used to evaluate

whether data has been generated according to specifications, to satisfy acceptance criteria, and to 

ensure data is appropriate and consistent with its intended use.

5.7

Data Compatibility

Data compatibility is an integral component to an effective DVI data management strategy. 

Compatibility means that data is in a format that can be exchanged with other parties. Ensuring 

compatibility of paper-based data is less complicated than ensuring compatibility of digital data, 

particularly for large scale incidents. For digital data, compatibility can be assumed if the data 

adheres to common digital data exchange standards.

5.8

Data Centralization/Analysis

In order for the data to be useful, the data must be centralized so that the comparisons can be made. 

Data analysis includes the selection, evaluation, and interpretation of data as a means to develop 

conclusions. The appropriate analysis of DVI data, which includes the comparison and matching of 

the antemortem and postmortem data, is fundamental to a successful DVI operation. The SWGDVI 

Reconciliation and Quality Assurance guidelines address the comparison of antemortem and 

postmortem data in greater detail.

5.9

Data Reporting

Data reporting involves the communication of results and conclusions drawn from the data analysis 

to stakeholders. The stakeholders may be the families, DVI response participants, media and 

general public, elected officials, government support agencies, incident management, etc. It is 

important that DVI agencies have a strategy for data reporting that provides the stakeholders with 

the information they need while ensuring the appropriate confidentiality for the victims and their 

families.

5.10

Data Exchange

Data exchange addresses the policies and data format standards necessary for data compatibility to 

allow for the effective interchange of data between systems. This is an essential component of DVI 

data management, as the efficient and effective exchange of data facilitates the acquisition of data 

from various sources and the comparison of antemortem (AM) and postmortem (PM) data 

necessary for victim identification. The best practices and standards discussed in the remainder of 

this document will concentrate on the application of existing data exchange standards for softwarebased DVI data management systems.

6 Requirements

With appropriate resources, DVI data management systems can be an efficient and effective tool to 

facilitate the collection, validation, exchange, analysis, and reporting of DVI data. Non-DVI specific 

data standards have not historically been applied to DVI data, however, they are applicable to DVI 

data management systems. Current DVI data management standards are listed in Section 5.3 below. 

DVI data management systems that adhere to common standards: support individual jurisdictions’ 

efforts to achieve identifications, support the globalization of DVI data compatibility which will 

benefit all medicolegal jurisdictions, and strengthen the accuracy and efficiency of identifications 

due to the compatibility and fidelity of recorded/reported antemortem and postmortem data to the 

actual antemortem and postmortem characteristics of the individual(s). The following are best 

practices for DVI data management.
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6.1 Data Management System Components

Much has been learned from the development of data management systems2 and their application 

following recent mass fatality incidents around the world. These lessons have led to the 

identification of specific capabilities that facilitate effective DVI data management. There 

is considerable overlap between DVI data and routine daily decedent case management 

data, although the same data may have different applications for DVI than for decedent case 

management (and often when the DVI surge is over, the remaining cases, e.g. unidentified bodies 

and families who have not been repatriated with human remains may be incorporated 

with daily case management systems). As such, DVI data should be managed in such a way 

that allows for communication with decedent case management systems. A consensus list of the 

components that constitute an effective DVI data management system are outlined below. The list is 

divided broadly into antemortem, postmortem, victim identification, and fatality surveillance, 

with some main components further divided into relevant subcategories.

Antemortem DVI Data

o Disaster missing persons reporting

o Victim list development

o Victim Information Center/Family Assistance Center data management 

Postmortem DVI Data

o Scene data management

o Morgue data management 

Victim Identification Data 

Fatality Surveillance

6.1.1 Antemortem DVI Data

Antemortem data management can be divided into the following subcategories:

Disaster missing persons reporting 

Victim list development

Victim Information Center/Family Assistance Center (VIC/FAC) operations

The above listed subcategories are not listed in operational order, and the operational order may vary 

based on the incident characteristics (e.g. open versus closed population). The following are best 

practice recommendations for data that should be included under each of these headings.

6.1.1.1 Disaster Missing Persons Reporting

Mass fatality incidents typically result in a massive surge in the number of missing persons (MP) 

inquiries/reports in the immediate hours following an incident with which the local law 

enforcement and medicolegal authority must contend. These initial inquiries/reports provide the 

first potential access to the antemortem data required to identify decedents. Because many of these 

individuals may be temporarily displaced and not dead, mismanagement of this data may 

undermine the identification effort and create unnecessary frustration on the part of the families of 

the deceased and missing. For this reason an effective disaster missing persons reporting function is 

a critical component of a DVI data management system. The entire missing persons reporting and 

resolution process often involves multiple agencies and the responsibility for the maintenance of 

this component of DVI data management may reside with law enforcement, the medicolegal

2

Aseparatedocument entitled “Data Management:GuidelinesforInformation Technologists” isplanned forissuance by theSWGDVIbased onguidancepublished 

inthe 2013Update totheAmerican National Standard forInformation Systems/National Institute of Standards and Technology-Information Technology Laboratory 

(ANSI/NIST-ITL) standard. This document will contain technical best practices for the development of a data management software application presented in 

alanguage intended forinformation technologists. TheNISTwilldevelop theaforementioned documentinpartnership withtheData Management Committee ofthe 

SWGDVI.
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authority, or another authorized entity. Table 1 presents a list of the capabilities that constitute a 

robust disaster missing persons reporting function within a DVI data management system. The 

disaster missing persons reporting function is divided into Call Center/Data Collection Center and 

internet-based reporting functions.

6.1.1.1.1 Call Center/Data Collection Center

A Call Center/Data Collection Center involves a mechanism by which individuals are able to report a 

person missing. This reporting mechanism may be implemented through a call center, requiring a 

large phone bank and phone operators. The call center allows family and friends of missing persons 

to report MPs via phone to a phone bank operator who is trained to acquire the appropriate 

information. Alternatively, a data collection center can provide the same services when a call center 

is not available or practical. A data collection center is a physical location to which families of the 

missing can report to provide information about the MP. The data collection center may be 

collocated with the Family Assistance Center as needed. In both instances, reports may be collected 

by a party that is independent of the identification effort, who then forwards the MP information to 

the relevant authority for investigation. Whether the interviews are conducted by operators over 

the phone or by data collectors in person, they should be streamlined to effectively and efficiently 

capture only the following data:

Contact information from the person making the report (i.e. the reporter) 

Investigative contact data for the missing person(s)

o Place of residence

o Place of employment

o Phone numbers

o Relationship to person making the report (e.g. caller primary next-of-kin, 

distant

relative, or life partner)

o Identification type(s) and number(s) (i.e. Social Security Number, Driver’s

License

Number)

Summary information regarding the circumstances surrounding the disappearance of 

the MP (ideally, this information should be recorded in such a fashion that the end users 

of the system would be able to obtain an assessment of the likelihood that the person 

reported missing was actually involved in the MFI)

The system should facilitate efficient data collection by:

Utilizing a single phone number/contact point for all MP inquiries 

Providing confirmation to the reporter that a report has been received

Ensuring timely forwarding of the data pertaining to the MP and the reporter to 

the local law enforcement agency and medicolegal authority (these data will facilitate 

investigative processes and subsequent contact with next of kin [NOK])

6.1.1.1.2 Internet-based Reporting Functions

DVI data management systems ideally should also facilitate publicly accessible, internet-based 

reporting capabilities by the family and friends of missing persons. If possible, these systems should 

be compatible with mobile devices. An effective DVI data management system should:

Generate a MP report based on data collected from reporters

Allow for the recording of multiple contact methods and addresses for each reportee 

Include the capability to provide a receipt verifying that a report has been filed remotely
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The entire missing persons reporting and resolution process often involves multiple agencies and 

the responsibility for the maintenance of this component of DVI data management may reside with 

law enforcement, the medicolegal authority, or another authorized entity. Table 1 presents a list of 

the capabilities that constitute a robust disaster missing persons reporting function within a DVI 

data management system.

6.1.1.2 Victim List Development

The reported missing persons data collected from the Call Center/Data Collection Points, the 

internet-based reporting functions, the investigative information from law enforcement, and the 

postmortem information from the medicolegal authority should be incorporated into a single 

missing persons list. The volume of data associated with large-scale mass fatality incidents may be 

difficult to manage, and efficient data management should include a strategy for effective data 

consolidation. For this reason, an effective DVI data management system will incorporate a victim list 

development function. This function will pare down missing persons data by detecting and 

resolving duplicate reports and verifying the status of persons reported missing. The victim 

list development process requires data verification and consolidation, and the end result of the 

process is a complete and verified electronic list of missing persons. The victim list development 

function should include a list management function and report verification function.

6.1.1.2.1 List Management Function

The list management function facilitates the detection and resolution of missing persons data 

duplication. Data mining and report searching capabilities are important components of an effective 

list management function. The system should be able to accommodate these capabilities in a multijurisdictional large-scale incident with multiple users and multiple locations. It should also be 

capable of sending automatic notification of detailed missing persons data to all users, even in multijurisdictional contexts.

6.1.1.2.2 Report Verification Function

The report verification function involves the facilitated reconciliation of missing persons reports. 

This function should be capable of providing confirmation of missing persons status when system 

queries are made, information that cases can be marked as closed or completed as individuals are 

reported found or are identified, records searches by any data field or combination of fields, 

generation of missing persons statistics, and capable of converting and uploading data provided by 

air carriers and other entities that have a verified manifest. Recommended specific functions within 

the victim list development capability are listed in Table 2.

6.1.1.3 Victim Information Center/Family Assistance Center Data Management

A Family Assistance Center function is a recommended component of a DVI data management 

system. This component should facilitate the collection of antemortem data at the VIC as well 

as efficienttransfer ofthis data to the medicolegal authority. The system should minimize the number 

of antemortem interviews conducted through the efficient application of the victim list process. 

There are two phases to this process: 1) victim list development and 2) antemortem data collection. 

The collection of antemortem data can only begin after the victim list development process 

has initiated. Although the victim list development process does not need to be completed 

before antemortem data collection can begin, the development of list drives the antemortem 

data collection process, and must be initiated first. Recommended functions within the 

VIC/FAC component are listed in Table 3.
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6.1.2 Postmortem DVI Data

Postmortem DVI data can be divided into the following subcategories: scene data and morgue data. 

The following are best practice recommendations for the data types that should be included under 

each of these headings.

6.1.2.1 Scene Data Management

Mass fatality incident scene data is critical to the integrity of the victim identification process. It is 

crucial that the appropriate data are captured in a format that facilitates comparison to morgue and 

VIC/FAC data. A DVI data management system should accommodate all scene materials including site 

maps, text, photographs, video, and scanned documents. Data management strategies should 

include a tracking capability through systematic coding of cases for the maintenance of chain 

of custody for all evidence related to identification efforts and associated data. This process can 

be enhanced through the use of barcodes or radio frequency identification devices (RFID). Ideally, the 

system should have the ability to accommodate multiple recovery locations/scenes multiple 

concurrent incidents, multiple jurisdictions, as well as accommodate multiple jurisdictions’ case 

number systems. Movement/transfer of possible human remains (PHR) should always be recorded. 

Table 4 lists recommended scene data management capabilities.

6.1.2.2 Morgue Data Management

Morgue data are also critical to the DVI process, and in the case also the appropriate data should be 

collected and captured in a format that facilitates comparison to antemortem data. Ideally, a DVI 

data management system should accommodate PHR intake, accessioning, and processing of data 

collection by multiple jurisdictions. The system should be capable of generating a unique identifier 

that can be cross-referenced to multiple case numbering schemes in cases where multiple 

jurisdictions are involved. The morgue data management function should also be able to 

accommodate the exchange, storage, and protection of postmortem photographs, radiographs, 

fingerprints, dental, and DNA data. Table 5 lists recommended morgue data management 

capabilities.

6.1.3 Victim Identification Data

The process of comparing antemortem, postmortem, scene, and contextual data to achieve 

identification is the core function of the DVI process. This document does not make 

recommendations regarding appropriate identification methods or appropriate thresholds for 

identification, but provides guidance regarding the applicable standards that apply to whatever 

method(s) a medicolegal jurisdiction employs. An effective data management system should have 

data reconciliation capabilities as well as allow the searching of any data fields. The system should be 

able to recognize body part duplication and suggest exclusions if sufficient triage data is present. The 

system should accommodate data formats pertinent to scientific identification, including dental, 

fingerprints, radiographs, and DNA. The data management system should also be able to import, 

store, and export these data from different systems. A DNA matching solution should be able 

to validate kinship. Table 6 lists recommended identification capabilities related data 

management.

6.1.4 Fatality Surveillance

Preliminary reporting of fatalities and operational progress are important components of a data 

management system as stakeholders often require metrics to gain situational awareness and to 

develop and maintain a response strategy. Reliable and efficient accounting of the preliminary 

number and circumstances of deaths related to a particular incident is an important component of 

mass fatality incident response planning. This is of particular importance in widespread multi-
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jurisdictional and/or prolonged responses. An effective DVI data management system should 

incorporate a surveillance/ reporting component that facilitates the acquisition and consolidation of 

death reports from a variety of sources including hospitals, healthcare facilities, and law 

enforcement sources. This function should have a data mining capability that can utilize publicly 

available data to generate a tentative estimation of incident-related fatalities, and should be able to 

automatically detect duplications and redundancies as well as indicate whether or not a particular 

death is subject to the medicolegal authority’s jurisdiction. The system should have automatic 

report generation capabilities for the medicolegal authority and should be able to identify the 

appropriate medicolegal geographic jurisdiction for the report. It should be able to generate fatality 

statistics rapidly. Table 7 identifies the best practice capabilities of a fatality surveillance function.

6.2 DVI-Relevant Data Exchange Standards

There are existing data exchange standards that can be applied to DVI data management. The 

relevant exchange standards are defined below.

6.2.1 ANSI/NIST-ITL 1-2011 500-290 Version (2013)

The Information Technology Laboratory (ITL) of the National Institute of Standards and 

Technology (NIST) provides technical leadership and fosters collaborative research in a variety of 

technology contexts in the interest of overcoming barriers to usability, scalability, interoperability, 

and security in information and networks. The document entitled ANSI/NIST Special Publication 

500-290, Data Format for the Interchange of Fingerprint, Facial and other Biometric Information 

specifically addresses the biometric data commonly used in DVI operations (1). The scope of this 

document is to define the content, format, and units of measurement for the electronic exchange of 

fingerprint, palm print, plantar, facial/mugshot, scar, mark and tattoo, iris, dental, DNA, and other 

biometric and forensic information used in the identification or verification process of an 

individual, and is intended for use by criminal justice administrations or organizations that rely on 

biometric or forensic data for identification purposes.

Several profiles of this standard are in use by major agencies and organizations around the world 

(e.g. INTERPOL, NATO, Royal Canadian Mounted Police, European Union). For instance, in order to 

exchange fingerprint and facial data with the Federal Bureau of Investigation (FBI), the Electronic 

Biometric Transmission Specification (EBTS) defines which fields of the ANSI/NIST-ITL standard 

format are required (10). The US Department of Defense (DOD) has a slightly different version of 

EBTS (11). Several states, such as Florida and Texas, also have established their own profiles of the 

ANSI/NIST-ITL standard.

6.2.2 NIEM

The National Information Exchange Model (NIEM) is a partnership between the US Department of 

Justice and the Department of Homeland Security. NIEM is designed to provide a common semantic 

approach for data transmission. DVI related biometric data are incorporated into the biometrics 

domain of NIEM, which is managed in coordination with ANSI/NIST-ITL. The NIEM Biometrics 

domain supports biometric-related services and mission-based activities, such as homeland 

security, national defense, border management, immigration benefits, and global law enforcement 

through the joint development and alignment of Extensible Markup Language (XML) Biometric 

Standards. In 2013, the NIEM biometrics domain was established to harmonize XML naming 

conventions for biometrics-related applications. It is closely linked with the ANSI/NISTITL organizational format and is fully conformant to the NIEM biometrics domain. (12)

6.2.3 DICOM

Digital Imaging and Communications in Medicine (DICOM) is an accredited international standard 

published through the National Electrical Manufacturers Association (NEMA). In dental
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applications, medical images and associated data are both stored in the DICOM file format which 

can be transmitted by the ANSI/NIST-ITL standard for use in DVI operations. A DICOM reader is 

needed to view and interpret the information; free readers are available for this application. (13)

6.3 Adherence to Existing Data Exchange Standards/Guidance

The best practice for medicolegal authorities or other agencies who intend to adopt or develop a 

DVI data management system is to abide by applicable existing data exchange standards. The 

paragraphs below identify the appropriate standards for the exchange of DVI data. Adherence to 

these standards will facilitate compatibility between existing and future DVI solutions.

Extensible Markup Language (XML)is the most appropriate markup language for the exchange of text 

based DVI data. Novel data management systems should be constructed with, or be compatible with 

XML. The ANSI/NIST-ITL standard is encoded in either Traditional (binary) or NIEMconformant XML. Traditional format is what is most commonly in use, but newer systems are 

encouraged to switch to XML.

6.3.1 DVI-Relevant Data Collection Standards

Medicolegal authorities that are developing or acquiring a DVI data management system should be 

aware that relevant standards for data exchange exist, and systems should be conformed to ensure 

that the DVI process can effectively generate identifications. Organizations (such as the FBI or 

Interpol) that will receive data from a medicolegal authority require that the ANSI/NIST-ITL 

standard be used for data interchange. The standard describes the interchange as “transactions” 

involving the exchange of “records”; “records” are described as defined sets of fields which may be 

specified by the standard to be either mandatory or optional that contain data as defined by 

the ANSI/NIST-ITL standard. Examples include data capture, storage, and transmission 

requirements such as: fingerprints shall not be captured, stored or transmitted at less than 500 

pixels per inch (PPI), and that such 500 PPI images are stored in WSQ format.

Each record type in ANSI/NIST-ITL described below can contain a hash of the data contained in that 

record. ANSI/NIST-ITL Type 98 has additional data security for the entire transaction (objects 

associated with the individual). In addition, the ANSI/NIST-ITL Type 98 (Information Assurance) 

record provides guidance regarding data protection and security. The ANSI/NIST-ITL Type 98 

record contains security information that assures the authenticity and/or integrity of a transaction. 

The Type 98 record applies to all non-Type 98 records.

6.3.1.1 Demographic data

The demographic data collected during the missing person report, antemortem interview, and 

postmortem examination processes should be handled using the ANSI/NIST-ITL Standard (typically 

in the Type 2 Record).

6.3.1.2 Fingerprint data

The fingerprint data collected during the antemortem interview and postmortem examination 

processes should be handled using the ANSI/NIST-ITL Standard (Types 4 and 14 Records).

6.3.1.3 Dental data

The dental data collected during the antemortem interview and postmortem examination processes 

should be handled using the ANSI/NIST-ITL Standard (Type 12 Record).

6.3.1.4 Image data

The image data (including images of the face, scars, [needle] marks, and tattoos [SMT], and other 

body parts, non-dental photographs) collected during the missing person report, antemortem
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interview and postmortem examination processes should be handled using the ANSI/NIST-ITL 

Standard (Type 10 Record). The Type-10 record also includes the ability to transmit and describe 

images of suspected patterned injuries. Radiographic information and other non-visible light 

images are handled using the ANSI/NIST-ITL Standard (Type 22 Record).

6.3.1.5 DNA data

The DNA data collected during the missing person report, antemortem interview, and postmortem 

examination processes should be handled using the ANSI/NIST-ITL Standard (Type 18 Record).

6.3.1.6 Other biometric data

There are other record types in the ANSI/NIST-ITL standard to transmit other biometric data types 

such as palm and plantar prints (Types 15 and 19 Records). Although these are not commonly used 

there are some small databases that may be available to medicolegal authorities. The ANSI/NIST- ITL 

standard also includes the capability to transmit iris data (Type 17).

6.3.1.7 Non-biometric data

There are also additional records for non-biometric data, such as Type 21, that may be useful 

to medicolegal authorities. Type 21 includes the ability to transmit non-biometric associated images 

of personal effects and associated data for medical devices.

Table 8 identifies the appropriate ANSI/NIST-ITL standards for the various data types that are 

associated with a DVI investigation in tabular format.

7 Tables

Table 1 – Disaster Missing Persons Reporting Functions 

Missing Persons Call Center/Data Collection Center

Data Collection Center facilities and/or structures (e.g. large phone bank) 

Single phone number or location advertised to the public

Standardized missing persons script for operators/staff

Just-in-time training for operators/staff

Capability to generate a missing persons report for each missing person (consolidation of multiple 

reports for one individual)

Accommodate reports from multiple incidents 

Coordinate reports from multiple locations

Accommodate a single reporter reporting multiple missing persons

Automatically forward MP data to appropriate law enforcement, medicolegal authority, and FAC 

Foreign language translation capability

Allow for the collection of multiple contact methods/means per case

Provide relationship/kinship filtering (i.e. male callers only search for father, brother etc.) 

Searchable fields including free text

Capability to score/grade MP reports for comparison todecedents 

Receipt confirmation of report completion

Multi-jurisdictional data sharingcapability

Missing Persons Internet-Based Reporting Functions

User friendly interface

Capability to handle multiple missing person reports, possibly involving incidents in multiple 

locations during a single session
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Provide email/text confirmation of report receipt to the reporter 

Accommodate up to thousands of users

Mobile device compatible

Missing Persons Database Functions

Receive data from call center, internet-based reporting, law enforcement, etc. 

Capability to operate from multiple locations

Accommodate multiple incidents 

All fields in database searchable

Generate activity log that records all changes to reports

Identify and display “like” cases (preliminary MP reconciliation)

Capacity to develop missing persons statistics (missing persons tallies etc.) 

Ability to prioritize or triage missing persons reports

Table 2 – Victim List Development

List Management Functions

Data report analysis function

Capability to generate multi-jurisdictional fatality reports (automatic notification) 

Generate possible matches to facilitate the reconciliation process

Accommodate multiple concurrent users 

Weighted report ranking

Data mining capability (searchable by specific report criteria) 

Generate reports for any searchable criteria

Report consolidation

Report Verification Functions

Provide confirmation of MP status

Delete records, but retains record in log format if missing person is found alive 

Search any missing persons by any field

Generate missing persons statistics

Capability to convert and upload a verified manifest provided by air carriers or other entities

Table 3 – FAC Data Management

Antemortem Data Collection

Capability to search missing persons data from law enforcement agencies and hospitals (monitor 

patient tracking)

Provide credentialing for responders and families entering the VIC/FAC 

Manage interview scheduling

Track NOK visits to VIC/FAC

Provide standardized antemortem interview questions to direct interview specifics 

Accommodate scanned documents

Track outstanding antemortem data requests (lack of antemortem interview information; 

data requests from family members; data requests from external entities)

Track chain of custody for items submitted by NOK

Provide standardized automated coding capability (with barcode auto-population of 

predetermined fields)

Facilitate mailing items to NOK

Accommodate multi-jurisdictional data transfer for large scale incidents
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Accommodate collection/tracking of photographs, radiographs, fingerprints, dental, and DNA data

Antemortem Data Reporting 

Generate simple reports 

Report FAC interview statistics

Document the status of items submitted by NOK 

Tracks NOK Notification Status/Preferences 

Maintain log of NOK contacts

Track NOK notification of identification preferences (e.g. every time an identification is made, first 

time an identification is made, end of process, beginning and end, never)

Table 4 – Scene Data Management Function 

Accommodates Scene Documentation

Integrate with mapping data or maps developed using other systems

Capability to collect basic decedent location information in multi-jurisdictional responses 

Accommodate the exchange/storage/protection of scene photography/video

Include barcode/RFID compatible tags

Accommodate the exchange/storage/protection of fingerprint data

Manage multiple case number systems in case the MFI involves multiple jurisdictions

Documents Human Remains Site Information

PHR description 

Site description

Manages PHR Storage at Collection Site 

Document PHR handling (personnel) 

Document PHR relocation

Document PHR transport

Manage unassociated evidence chain of custody

Accommodate the exchange/storage/protection of evidence photos

Tracks Personal Effects

Log associated personal effects and manages chain of custody 

Log unassociated personal effects and manages chain of custody

Table 5 – Morgue Data Management Function 

Automated Fatality Reporting Capability

Facilitate reporting of fatalities to multiple jurisdictions (for widespread incidents) 

Facilitate general MFI status reporting to multiple jurisdictions

Facilitate automated decedent ID status reporting (notification of identification status)

Manages Human Remains Intake/Accessioning

Capability to manage multiple remains collection points or morgue locations associated with 

a single incident

Human remains tracking through morgue process 

Automated tracking capability (i.e. barcode, RFID) 

Possible Human Remains Case Numbering 

Generate case numbers

Manage multiple case number systems
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Identify/prevent possible case number discrepancies

Morgue Data Capability

Accommodate exchange/storage/protection of postmortem photographs 

Accommodate exchange/storage/protection postmortem radiographs 

Accommodate exchange/storage/protection of fingerprint data

Accommodate exchange/storage/protection of DNA sample collection information 

Accommodate exchange/storage/protection of dental data

Station-Based Morgue Operations 

Sample tracking (toxicology, DNA etc.) 

Postmortem exam component

Supports Postmortem Exam Data Entry

Support anthropology exam data entry 

Support dental postmortem exam data entry

Support pathology postmortem exam data entry

Record postmortem exam administrative data (who performed exam, etc) 

Accommodate morgue tracker (escort) process

Records Final Disposition Data

Funeral home data

Disposition location (i.e. GPS coordinates)

Table 6 – Identification Data Management Function 

AM/PM Data Reconciliation

Rank-order possible matches based on available AM/PM data 

Search based on any/all antemortem fields

Search based on any/all postmortem fields 

Suggest exclusions based on available AM/PMdata 

Facilitates ID Tracking

Generate ID reports

Accommodate exclusionary DNA samples

Facilitates Re-Association of HR Fragments

Facilitate linking/unlinking HR by PM criteria (body part duplication etc) Capability to 

enter in exclusions to prevent duplicate review of possible matches Facilitates 

Fingerprint Data Exchange Conformant with ANSI/NIST-ITL Standards 

Accommodate electronically gathered fingerprints in ANSI/NIST-ITL format 

Accommodate scanned copies of paper fingerprints

Transmit fingerprint data to various databases automatically (if possible: e.g. FBI requires

different information than INTERPOL. Even though they are both ANSI/NIST-ITL conformant, they

have different ‘profiles’)

Generate fingerprint comparison reports

Facilitates Radiographic Exchange Conformant with ANSI/NIST-ITL Standards

Accommodate digital skeletal and dental radiographs 

Accommodate scanned radiograph films
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Facilitate AM/PM radiograph comparison 

Generate radiograph comparison reports

Facilitates DNA Data Exchange Conformant with ANSI/NIST-ITL Standards

Capability to accommodate DNA data for various analysis types (autosomal STR, Y-STR, 

mitochondrial DNA, etc)

Capability to accommodate complex DNA matching results, including kinship analysis, generated 

by external software

Generates DNA matching reports

Facilitates Collection of Information Needed for Death Certificates

Cause and manner of death

Decedent information (e.g. name date, location, and time of death)

Table 7 – Fatality Surveillance

Data mining component that can utilize both official (hospitals and law enforcement) and publicly 

available (media) data to identify deaths related to a particular incident

Data reconciliation component that eliminates duplicate and/or redundant death reports 

Identify medicolegal cases via detection of “official” vs. unofficial reports when possible 

Generate automatic reports to all users

Rapid generation of fatality statistics

Forward reports to appropriate medicolegal jurisdiction 

Generate reports regarding circumstances of death

Table 8 – ANSI/NIST-ITL Standards for DVI Investigations

Type Applicable Standards

Demographic data ANSI/NIST-ITL Type 2 Record as specified in their application profiles 

(EBTS for FBI and DoD; INT-I for INTERPOL)

Fingerprint data ANSI/NIST-ITL Type 4 or Type 14 records 

Dental data Dental Data ANSI/NIST-ITL record Type12.

Dental radiographs DICOM images transmitted through ANSI/NIST-ITL record Type 22or 

scanned images directly through ANSI/NIST-ITL Type 22

Image data Visible images and patterned injuries use ANSI/NIST-ITL Type 10; 

Radiographic information and other non-visible light images arehandled 

using the ANSI/NIST-ITL Standard (Type 22 Record)

DNA data CODIS & ANSI/NIST-ITL Type 18 record

Other biometric 

data

Non-biometric 

associated images

Palmprints: ANSI/NIST-ITL Type 15; footprints: ANSI/NIST-ITL Type 19; 

Scars/tattoos/injuries/deformities/piercings (images): ANSI/NIST-ITL 

Type 10

ANSI/NIST-ITL Type 21 for images of personal effects, and the type, make, 

model and serial number (if applicable) for any medical devices found in/on 

a person
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Annex A (informative)

Foundational Principle

DVI data management systems should be designed to facilitate the collection, storage, comparison, 

and reporting of missing person (antemortem) data and decedent (postmortem) data in order to 

achieve a scientifically reliable identification. A DVI data management strategy is a collection 

of processes that may include, but is not limited to, policies, procedures, data, and for most situations, 

the various DVI and supporting software required to support the operation. DVI data management 

strategies should be effective, reliable, scalable, usable, and interoperable. Ideally, this strategy 

should not be limited to mass fatality incident responses but should also be incorporated into daily 

operations. DVI data can take many forms, ranging from the use of paper records to highly complex 

digital data. The preferred means for managing DVI data is dependent on the scale and complexity of 

the incident. There exist data exchange standards that have not historically been applied to DVI data, 

but that are applicable and should be adhered to (e.g. National Institute of Standards and 

Technology [NIST] ANSI/NIST-ITL 1-2011, NIST Special Publication 500-290 Data Format for the 

Interchange of Fingerprint, Facial & Other Biometric Information)(1).
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Mass Fatality Incident Data Management:

Best Practice Recommendations for the Medicolegal Authority

Annex B

(informative)

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