Disaster Response, Relationship to ED Patient Distribution and Pharmacy Requirements

1
Disaster Response, Relationship to ED Patient
Distribution and Pharmacy Requirements

• Marc S Rosenthal, PhD, DO, FACEP
• Rob Dunne, MD and Bill Drake, PharmD
• Michigan-1 DMAT
• Wayne State University

2
Introduction

• Lecture
• Disaster Team Modeling
• Marc Rosenthal
• Panel Discussion
• Disaster Team Pharmacy Needs
• Rob Dunne
• Bill Drake
• Marc Rosenthal

3
Introduction

• Introduction
• Reasons
• Type of Disasters
• Response Types
• Worst Case
• Expected Patient Population
• Expected Needs
• Conclusion

4
Introduction

• Obvious
• Better prepared, easier to provide care
• Large variety in disasters
• Costly and difficult to stock for a disaster
• Equipment
• Disposables
• Medications
• Need for better planning

5
Introduction

• Reasons
• Improved caches can meet the needs of teams and
  patients
• Faster care and more appropriate care
• Less waste
• More control on expenses
• Decreased morbidity and mortality with an
  appropriate match of resources to needs.

6
Disasters

• Types of Disasters
• Highly varied in mechanisms
• Can have significant variations in needs
• Food and water
• Shelter
• Medical needs
• Communications
• But also have common thread

7
Disasters

• Natural
• Flooding
• Earthquakes
• Hurricanes / Typhoons
• Tornadoes
• Blackouts
• Tsunamis
• Volcanic Eruptions
• Infectious
• E.g. pandemic flu

8
Hurricanes
9
ManMade
10
Accidental
11
Accidental-Before
12
Disasters

• ManMade
• Explosions
• Chemical Releases
• Fires
• RDD
• Chemical Attacks
• Biological attacks

13
Disasters

• Common Threads
• Shelter
• Food and water
• Environmental issues
• Heat
• Cold
• Rain
• Transportation
• Communications
• Medical Care

14
Disasters

• Medical Care
• Can be varied
• Malnutrition
• Trauma
• Initial event
• Post-event
• Civilian unrest/opportunity
• Infectious disease
• Medication refills, chronic conditions

15
Disasters

• Medical Care
• Loss of normal access to care
• Need to replace normal access to care
• Local population will have baseline medical
  needs, undifferentiated from the disaster
• Increase volume from disasters
• Includes loss of medications
• Worried well
• Injuries/illness directly related to the event

16
Disasters

• Medical Care
• Event Changes
• Increased minor and major trauma
• Increased GI illness due to contamination
• Illness directly related to event
• E.g., WMD, Chemical exposure
• WMD, Biological, Anthrax

17
Planning

• How do you plan for a disaster response
• Plans
• Local, on site hospital or clinic
• Federal/regional response team
• Local
• Question of sufficient medications
• Depends on event
• Question of sufficient food and water

18
Response

• Response Teams
• Augment or replace local resources to provide
  care
• Medications
• Medical personnel
• Equipment
• Shelter to provide care

19
Response Teams

• What is needed and how much
• Biggest question
• Can vary based on scenario
• Can assume a constancy between different events?

20
Event Variability

• Any forceful event will have trauma
• Any event will have displaced persons without
  medications
• Any event will have increased injuries and
  illness secondary to the event
• But the local population will continue to have
  the baseline medical problems and trauma.

21
Events

• Will continue to see
• Asthma
• COPD
• Chest Pain patients
• Cardiac arrests
• Pneumonias
• UTIs
• STDs
• Viral illnesses
• Psychiatric illnesses
• Abdominal pain
• strokes

22
Events

• If local population
• Not evacuated, medical needs will increase
• If evacuated, medical needs can decrease
• Not Evacuated
• See the baseline population problems
• See increased patient volume secondary to event
  and increased risks taken by the population.

23
Event Planning

• Disaster Response Concerns
• Need to treat the baseline population problems
• Need to treat the increased trauma and medical
  issues related to the event.

24
Event Planning

• How to develop resources to respond
• How much is needed?
• What can be used to develop a needs assessment
  prior to an event.

25
Event Planning

• Can use prior events for help
• Can develop models
• Remember each event is different
• Needs can be the same regardless of event type.

26
Models

• Review previous events, develop model based on
  needs following events
• Make assumption of type of needs and compare to a
  current model
• Develop an ad hoc model and test it against
  previous events.

27
Hypothesis

• A community ED will provide a framework for a
  disasters teams response in terms of chief
  complaints and medication usage within 3 SD.

28
Model

• Assumptions
• Populations basic needs do not change
• Emergency and urgent care needed
• Routine care can be delayed
• Increased needs to refill medications
• Increased illness or injuries depending on the
  event

29
Model

• Assume worst case scenario
• Can always provide less
• Can not provide more care if resources are not
  available

30
Hypothesis

• Disaster response teams will provide care and
  will have chief complaints similar to a community
  emergency department, assumes worst case
  scenario, starting 48-72 hours post event.

31
Model

• Worst Case Scenario
• Loss of all medical resources
• Hospitals
• Clinics
• Pharmacies
• Loss of easy transport of patients
• Population has minimally evacuated area
• Poor weather (very hot or very cold)

32
Model

• Base model on emergency department operations
• Can handle any problem
• Can stabilize
• Can function as urgent care
• Can provide medication refills
• Can deal with environmental issues

33
Model

• Develop model on assumed patient volume
• Volume dependent on human resources
• Partially dependent on patient numbers
• Assume re-supply or additional supply of
  resources in a timely manner
• Assume fine tuning of supplies based on event and
  needs assessment

34
Model

• Based on community ED
• Cities and rural EDs probably do not represent
  typical ED care
• Best, if based on average of needs from different
  regions
• Different regions
• Different protocols
• Medical problems
• Local preferences

35
Model

• Mid Michigan Community ED
• EM Residency
• ICUs present including neonatal and pediatric
• Large volume 80,000 visits per year
• Deals with rural and city population
• Large catchments area

36
Model

• Accessed
• Hospital information system
• Hospital supply system
• Hospital pharmacy control system
• ED discharge database
• ED information system

37
Model

• Recorded
• Ages and number of patients
• Chief complaints
• Diagnosis
• Medications used
• Discharge medications
• Priority of patients
• Procedures performed
• Number of codes

38
Model

• Data recorded for 1 year, reported month by month
• Can determine number of X per month, year or per
  patient
• Supplies, medications, chief complaints, age,
  diagnosis
• If assume patient volume, can then determine
  number of expected patients with expected chief
  complaint

39
Model

• Model tested against published disaster responses
  by the NDMS system
• Compared to New Mexicos published response to
  two hurricanes
• Compared to Michigan-1 response to Hurricane
  Katrina.

40
Model

• No model is all inclusive
• Each event will be different with different
  system stressors
• A model should be within 3 SD of actual event data

41
ResultsCommunity ED
42
(No Transcript)
43
Community ED
44
Hurricane Katrina
45
Community ED
46
Hurricane Katrina
47
(No Transcript)
48
(No Transcript)
49
(No Transcript)
50
(No Transcript)
51
Respiratory Data

• Community ED
• Nebulized treatments per pt.
• 0.118
• Expected number for 3 days
• 89
• Intubations per patient
• 0.004
• Expected number for 3 days
• 3

52
(No Transcript)
53
(No Transcript)
54
Observations

• Disaster needs can be highly varied.
• Community ED modeling can help plan disaster
  needs.
• Local populations following a disaster have the
  same needs profile as before the event.
• A community ED model does provide guidance on
  medication needs

55
Conclusion

• Modeling a community ED provides a first pass
  method to determine a disaster response teams
  medication cache requirements.
• Further evaluation of disasters is required to
  fine tune the model and develop better caches.

56
Discussion

• Questions
• Discussion by Panel