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The blackout in North America of August 2003 was one of the worst on record. It affected eight United States states and parts of Canada for >24 hours. Additionally, two large United States cities, Detroit, Michigan and Cleveland, Ohio, suffered from a loss of water pressure and a subsequent ban on the use of public supplies of potable water that lasted four days. A literature review revealed a paucity of literature that describes blackouts and how they may affect the medical community.
Methods:
This paper includes a review of after-action reports from four inner-city, urban hospitals supplemented accounts from the authors' hospital's emergency operations center (emergency operations center).
Results:
Some of the problems encountered, included: (1)lighting; (2) elevator operations; (3) supplies of water; (4) communication operations; (5) computer failure; (6) lack of adequate supplies of food; (7) mobility to obtain Xray studies; (8) heating, air condition, and ventilation; (9) staffing; (10) pharmacy; (11) registration of patients; (12) hospital emergency operations center; (13) loss of isolation facilities; (14) inadequate supplies of paper; (15) impaired ability to provide care for non-emergency patients; (16) sanitation; and (17) inadequate emergency power.
Discussion:
The blackout of 2003 uncovered problems within the United States hospital system, ranging from staffing to generator coverage. This report is a review of the effects that the blackout and water ban of 2003 had on hospitals in a large inner-city area. Also discussed are solutions utilized at the time and recommendations for the future.
Conclusion:
The blackout of 2003 was an excellent test of disaster/emergency planning, and produced many valuable lessons to be used in future events.
This study intended to create symptom-based triage algorithms for the initial encounter with terror-attack victims. The goals of the triage algorithms include: (1) early recognition; (2) avoiding contamination; (3) early use of antidotes; (4) appropriate handling of unstable, contaminated victims; and (5) provisions of force protection. The algorithms also address industrial accidents and emerging infections, which have similar clinical presentations and risks for contamination as weapons of mass destruction (WMD).
Methods:
The algorithms were developed using references from military and civilian sources. They were tested and adjusted using a series of theoretical patients from a CD-ROM chemical, biological, radiological/nuclear, and explosive victim simulator. Then, the algorithms were placed into a card format and sent to experts in relevant fields for academic review.
Results:
Six inter-connected algorithms were created, described, and presented in figure form. The “attack” algorithm, for example, begins by differentiating between overt and covert attack victims (A covert attack is defined by epidemiological criteria adapted from the Centers for Disease Control and Prevention (CDC) recommendations). The attack algorithm then categorizes patients either as stable or unstable. Unstable patients flow to the “Dirty Resuscitation” algorithm, whereas, stable patients flow to the “Chemical Agent” and “Biological Agent” algorithms. The two remaining algorithms include the “Suicide Bomb/Blast/Explosion” and the “Radiation Dispersal Device” algorithms, which are inter-connected through the overt pathway in the “Attack” algorithm.
Conclusion:
A civilian, symptom-based, algorithmic approach to the initial encounter with victims of terrorist attacks, industrial accidents, or emerging infections was created. Future studies will address the usability of the algorithms with theoretical cases and utility in prospective, announced and unannounced, field drills. Additionally, future studies will assess the effectiveness of teaching modalities used to reinforce the algorithmic approach.
In events such as earthquakes or terrorist attacks, hospitals may be victims of disasters. They may need to transfer patients to outside facilities rather than continue to provide on-site care. Following the Northridge earthquake, eight hospitals in the damaged area were the foci of a United States National Science Foundation study that examined the status of the hospitals' pre-event planning, post-event evacuationdecision-making, and internal and external evacuation processes. Building on this experience, this paper offers a standardized data collection tool, which will enable researchers to record hospital evacuation information in a systematic manner so that comparable data can be accumulated, evacuation research methods can be improved, and consensus on methods can be reached. The study's principal subjects include: (1) hospital demographics; (2) description of existing disaster response plans; (3) an event's impacts on hospital operations; (4) decision-making and incident command; (5) movement of patients within the facility; (6) movement of patients to off-site institutions; and (7) hospital recovery.
Responses to disasters involve many factors beyond personnel, such as medical andnon-medical equipment and supplies. When disaster teams respond, they must do so with sufficient amounts of medicine and supplies to manage all of the patients expected for several days before re-supply. In order for this process to be efficient and expedient, accurate and advanced planning for supplies needed by disaster workers is necessary. These supplies must provide for general medical care and for hazard-specific problems.
Objective:
To develop a model that provides the framework for determining supply requirements for the National Disaster Medical System, Disaster Medical Assistance Teams, or other responding disaster teams in a civilian environment.
Methods:
A community hospital was modeled to determine patient characteristics when presenting to an emergency department (ED), including patient demographics and chief complaint, medications administered during the emergency department visit and prescribed at discharge, and laboratory tests ordered to assess disaster team supply requirements. Data were downloaded from a patient tracking software package and abstracted from various hospital data information systems. Data from the community hospital were compared with data published from two hurricane disasters by members of the National Disaster Medical System.
Results:
To the extent possible, the model predicted the proportion of patient complaints and, therefore, the medicine and supplies needed for the management of these patients.
Conclusion:
This model offers a first step in preparing disaster medical teams for deployment.