Students must submit an MS Word document detailing how they can leverage the information, covered in chapters 11-15, in their own professional healthcare career.
Students should do their best not to just view this as another assignment to complete. Instead, use it to truly start to identify how they will start to move forward with their healthcare career. For example, start to ask themselves: what specific job do I want and who do I need to contact to achieve that goal?
Chapter 11
Process Improvement and
Patient Flow
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Process Improvement (PI)
• Measuring and improving systems
• Systems
• Processes
• Subprocesses
• Tasks
• PI tools can be used at any level
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PDCA
• Plan: Define the entire process to be improved using
process mapping. Collect and analyze appropriate data for
each of element of the process.
• Do: Use process improvement tool(s) to improve the
process.
• Check: Measure the results of the process improvement.
• Act to hold the gains: If the process improvement results
are satisfactory, hold the gains. If the results are not
satisfactory, repeat the PDCA cycle.
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PDCA Graphically
4. Act to maintain it.
3. Check to make sure
it is working properly.
1. Plan your
corrective action.
2. Do it.
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Flow
• Theory of swift, even flow
• Process is more productive as:
• Speed of flow increases
• Variability of process decreases
• Example: advanced access
• Decreased time from request to appointment (speed)
• Decrease in no-shows (variability)
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Patient Flow
• Hospital flow is negatively affected by variability in “scheduled”
demand:
• Surgical admissions (scheduled)
• Medical admissions (emergency)
• When surgical admissions have high variability, backlogs and waiting
occur
• Improvements can be made through
• Load balancing
• Using the theory of constraints to identify and remove bottlenecks
• Automation
• Other tools contained in Chapter 11
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Actions to Improve Inpatient Flow
• Establish uniform discharge time
• Write discharge orders in advance
• Centralize oversight of census and patient movements
(care traffic control)
• Change physician rounding times
• Coordinate with ancillary departments on critical testing
• Coordinate discharge with social services
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Why Use Process Mapping?
• Provides a visual representation that offers an
opportunity for process improvement through
inspection
• Allows for branching in a process
• Provides the ability to assign and measure the
resources in each task in a process
• Is the basis for process modeling via computer
simulation software
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Process Mapping Basics
• Assemble and train the team.
• Determine the boundaries of the process (where does it start and
end?) and the level of detail desired.
• Brainstorm the major process tasks and list them in order. (Sticky
notes are often helpful here.)
• Once an initial process map (also called a flowchart) has been
generated, the chart can be formally drawn using standard symbols
for process mapping.
• The formal flowchart should be checked for accuracy by all relevant
personnel.
• Depending on the purpose of the flowchart, data may need to be
collected or more information may need to be added.
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Intensive
ED Care
High
Patient
Arrives
at the ED
Triage Clinical
Complexity
Vincent Valley
Hospital and
Health System
Emergency
Department (ED)
Patient Flow
Process Map
Low
Waiting
Admitting
Private
Insurance
Yes
Triage Financial
Private
Insurance
Waiting
No
Admitting
Medicaid
Nurse
History/
Complaint
Waiting
Exam/
Treatment
Waiting
Discharge
End
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Process Metrics
• Capacity utilization: the proportion of capacity actually being used.
It is measured as actual output/maximum possible output.
• Throughput time: the average time a unit spends in the process. It
includes both processing time and waiting time and is determined by
the critical (longest) path through the process.
• Throughput rate: the average number of units that can be processed
per unit of time.
• Service time or cycle time: the time to process one unit. The cycle
time of a process is equal to the longest task cycle time in that
process.
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Process Metrics (cont.)
• Idle or wait time: the time a unit spends waiting to be processed.
• Arrival rate: the rate at which units arrive at the process.
• Work-in-process (WIP), things-in-process (TIP), patients-in-process
(PIP), or inventory: the total number of units in the process.
• Setup time: the amount of time spent getting ready to process the
next unit.
• Value-added time: the time a unit spends in the process where
value is actually being added to the unit.
• Non-value-added time: the time a unit spends in the process where
no value is being added. Wait time is non-value-added time.
• Number of defects or errors
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Basic Process Redesign Techniques
• Eliminate non-value-added activities
• Eliminate duplicate activities
• Combine related activities
• Process in parallel
• Balance workloads
• Develop alternative process flow paths and contingency plans
• Establish the critical path
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Basic Process Redesign Techniques
(cont.)
• Embed information feedback and real-time control
• Ensure quality at the source
• Match capacity to demand
• Let the patient do the work
• Apply the theory of constraints
• Identify best practices and replicate
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Basic Process Redesign Techniques:
Use of Technology
• Health Information Technology
• AI and Machine Learning
• Digital Therapeutics
• Internet of Medical Things
• Wearables
• Virtual and Augmented Reality
• Computer Vision and Image Processing
• Facial Recognition
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Basic Process Redesign Techniques:
Use of Technology – 2
• Natural Language Processing – Medical Chatbots
• Robotic Process Automation
• Cobots and Robots
• 3D Printing
• Autonomous Vehicles and Drones
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Queuing Theory
• Queues (lines) form because of limited resources
• Queuing theory is used to determine the best balance
between customer service and economic
considerations
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Queuing Notation
A/B/c/D/E
Where:
A = Interarrival time distribution
B = Service time distribution
c = Number of servers
D = Maximum queue size
E = Size of input population
• When both queue and input population are assumed to be infinite, D and E are
typically omitted.
• M/M/1 = exponential service time distribution, single server, infinite possible
queue length, infinite input population, assumes only one queue
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Queuing Formulas
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Queuing Formulas (cont.)
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Little’s Law
Average throughput time =
People (or things) in the system/Arrival rate
Example
• Clinic serves 200 patients in an 8-hour day (or 25 patients per hour).
• Average number of patients in waiting room, exam rooms, etc., is 15.
15 patients/25 patients per hour = 0.6 hours in the clinic
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Discrete Event Simulation
• Built on queuing theory
• Basic simulation model
• Entities (patients)
• Queues (waiting lines)
• Resources (people, equipment, space)
• Based on states and events
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Discrete Event Simulation
Note: Created with Arena simulation software. M = exponential
distribution; MRI = magnetic resonance imaging
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Process Improvement Methods and
Tools
• Six Sigma (Chapter 9)
• Seven basic tools
• Benchmarking
• Poka-yoke
• Lean (Chapter 10)
• Value stream mapping
• Takt time
• Standardized work
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End of Chapter 11
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Chapter 12
Scheduling and Capacity Management
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Scheduling and Capacity Management
• Hospital census and resource loading
• Staff scheduling
• Job/operation scheduling and sequencing rules
• Patient appointment scheduling models
• Advanced access scheduling
• Operating and market advantages
• Implementing advanced access
• Metrics for advanced access
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Typical Daily Hospital Census
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Hourly Census
Patients in the System
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
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Staff Scheduling
• Optimization/mathematical programming (chapter 7)
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Riverview Clinic Urgent Care Staffing
Using Linear Programming (LP)
Objective: Minimize salary and benefit expenses while satisfying
nurses
• Five consecutive days, with two days off every seven days
• Schedules chosen by seniority
Sun
Mon Tues
Wed
Thurs
Fri
Sat
6
Nurses Needed/Day
5
3
3
3
4
Salary and
Benefits/Nurse-Day
($/day)
320 240 240
240
240
240 320
4
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LP Problem
• There are seven possible schedules (Sunday and
Monday off, Monday and Tuesday off, and so forth).
• Objective is to minimize:
Salary and benefit expense = ($320 × Sun. # of nurses)
+ ($240 × Mon. # of nurses) + ($240 × Tues. # of
nurses) + ($240 × Wed. # of nurses) + ($240 × Thurs. #
of nurses) + ($240 × Fri. # of nurses) + ($320 × Sat. # of
nurses)
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LP Problem (cont’d)
Subject to:
• The number of nurses scheduled each day must be greater
than the number of nurses needed each day.
• Sun. # of nurses 5
• Mon. # of nurses 4
• The number of nurses assigned to each schedule must be
greater than 0 and an integer.
• # A (B, C, D, E, F, or G) nurses 0
• # A (B, C, D, E, F, or G) nurses = integer
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Excel Solver Setup:
Minimize Salary and Benefit Expense
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Excel Solver Solution: Minimize Salary
and Benefit Expense
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Excel Solver Setup:
Maximize Nurse Satisfaction
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Excel Solver Solution:
Maximize Nurse Satisfaction
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Job/Operation Scheduling and Sequencing
Rules
• First come, first served (FCFS)
• Shortest processing time (SPT)
• Earliest due date (EDD)
• Slack time remaining
• Critical ratio (CR)
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Sequencing Rule Example
Job
Processing
Time
Due
Date
A
B
C
D
E
50
100
20
80
60
100
160
50
120
80
Slack
Critical
Ratio
How many possible sequences for five jobs?
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First Come, First Served
Sequence
A
B
C
D
E
Average
Start
Time
0
50
100
170
250
Processing
Time
50
100
20
80
60
Completion
Time
50
150
170
250
310
Due
Date
100
160
50
120
80
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Tardiness
First Come, First Served
(cont’d)
Sequence
A
B
C
D
E
Average
Start
Time
0
50
100
170
250
Processing
Time
50
100
20
80
60
Completion
Time
50
150
170
250
310
186
Due
Date
100
160
50
120
80
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Tardiness
0
0
120
130
230
96
Shortest Processing Time
Sequence
C
A
E
D
B
Average
Start
Time
0
20
70
130
210
Processing
Time
20
50
60
80
100
Completion
Time
20
70
130
210
310
148
Due
Date
50
100
80
120
160
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Tardiness
0
0
50
90
150
58
Earliest Due Date
Sequence
C
E
A
D
B
Average
Start
Time
0
20
80
130
210
Processing
Time
20
60
50
80
100
Completion
Time
20
80
130
210
310
150
Due
Date
50
80
100
120
160
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Tardiness
0
0
30
90
150
54
Sequencing Rule Example
Job
Processing
Time
Due
Date
Critical
Ratio
Slack
A
50
100
100 − 50 = 50
100/50 = 2.00
B
C
D
E
100
20
80
60
160
50
120
80
160 − 100 = 60
50 − 20 = 30
120 − 80 = 40
80 − 60 = 20
160/100 = 1.60
50/20 = 2.50
120/80 = 1.50
80/60 = 1.25
120 possible sequences for five jobs
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Slack Time Remaining
Slack for each job: A—50, B—60, C—30, D—40, E—20
Sequence
E
C
D
A
B
Average
Start
Time
0
60
80
160
210
Processing
Time
60
20
80
50
100
Completion
Time
60
80
160
210
310
164
Due
Date
80
50
120
100
160
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Tardiness
0
30
40
110
150
66
Critical Ratio (CR)
CR for each job: A—2.00, B—1.60, C—2.50, D—1.50, E—1.25
Sequence
E
D
B
A
C
Average
Start
Time
0
60
140
240
290
Processing
Time
60
80
100
50
20
Completion
Time
60
140
240
290
310
208
Due
Date
80
120
160
100
50
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Tardiness
0
20
80
190
260
110
Summary
Rule
Average
Completion Time
FCFS
SPT
EDD
SLACK
CR
*Best values
186
148*
150
164
208
Average
Tardiness
No. of
Jobs Tardy
Maximum
Tardiness
96
58*
54*
66
110
3*
3*
3*
4
4
230
150*
150*
150*
260
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Guidelines for Selecting a
Sequencing Rule
1. SPT is most useful for a very busy resource.
•
•
Some jobs may never be completed.
SPT often is used with another rule.
2. Use EDD when only small tardiness values can be
tolerated.
3. Use FCFS when there is excess capacity.
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Appointment Scheduling Models
Purpose is to balance the competing goals of:
• Maximizing resource utilization
• Minimizing waiting time
Four types:
• Block appointment
• Individual appointment
• Mixed block-individual appointment
• Combinations
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Bailey-Welch Schedule
Bailey-Welch
Individual Appointment
Time
# Scheduled
Time
# Scheduled
0:00
1
0:00
2
0:20
1
0:20
1
0:40
1
0:40
1
1:00
1
1:00
1
1:20
1
1:20
0
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Advanced Access
• Traditional scheduling systems
• Long times until next appointment
• High no-show rates
• Double/triple booking—queues form
• Advanced access
• Patients seen same day as request
• Reduces no-show rate
• Better continuity of care
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Implementing Advanced Access
• Obtain buy-in
• Predict demand
• Predict capacity
• Little’s law (chapter 11)
• Standardize and minimize types of visit times
• Assess operations
• Work down backlog
• Go live
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Advanced Access Metrics
• PCP match: percentage of same-day patients who see their
PCP
• PCP coverage: percentage of same-day patients seen by any
physician
• Wait time for next appointment (or third next available
appointment)
• Good backlog: appointments scheduled in advance because
of patient preference
• Bad backlog: appointments waiting because of lack of slots
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End of Chapter 12
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Chapter 13
Supply Chain Management
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Supply Chain Management
• What is supply chain management (SCM)?
• Why is SCM Important for healthcare organizations?
• Tracking and Managing Inventory
• Forecasting
• Inventory Models
• Inventory Systems
• Procurement and Vendor Relationship Management
• Strategic SCM
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Supply Chain Management (SCM)
• The management of all activities and processes related to
both upstream vendors and downstream customers in the
value chain
• Tracking and managing demand, inventory, and delivery
• Procurement and vendor relationship management
• Technology enabled
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Inventory
• Inventory is the stock of items held by the
organization either for sale or to support the
delivery of a service
• Inventory management answers three questions:
• How much to hold
• How much to order
• When to order
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Functions of Inventory
• To meet anticipated demand
• To level process flow
• To protect against stockouts
• To take advantage of order cycles
• To help hedge against price increases or to take advantage
of quantity discounts
• To decouple process steps
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Effective Inventory Management
• Classification system
• Inventory tracking system
• Reliable forecast of demand
• Knowledge of lead times
• Reasonable estimates of:
• Holding or carrying costs
• Ordering or setup costs
• Shortage or stockout costs
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ABC Classification System
• Classifying inventory
according to some measure
of importance and allocating
control efforts accordingly
• Pareto Principle
– A very important
– B moderately
important
– C least important
High (80%)
Annual
$ volume
of items
A
B
C
Low (5%)
Few
(20%)
Many
(50%)
Number of Items
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Inventory Tracking
• Track additions and removals
• Bar-coding
• Point of use or point of sale (POS)
• RFID
• Physical count of items
• Periodic intervals
• Cycle count
• Find and correct errors
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Forecasting
• Exercises
• Averaging methods
• Trend, seasonal, and cyclical models
• Model development and evaluation
• VVH example
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Forecasting
Exercise 1
• Identify the pattern and construct a formula that will
“predict” successive numbers in the series.
• What is the next number in the series?
(a) 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7
(b) 2.5, 4.5, 6.5, 8.5, 10.5, 12.5, 14.5, 16.5
(c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5
• What is the formula for the next number in the
series?
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Exercise 1—Graphs
Series b
18
16
14
12
10
Series1
8
6
Series a
Series c
4
4.4
2
10.0
4.2
0
9.0
1
4.0
2
3
4
5
6
7
8
8.0
7.0
3.8
Series1
3.6
6.0
Series1
5.0
3.4
4.0
3.2
3.0
3.0
2.0
1.0
2.8
1
2
3
4
5
6
7
8
0.0
1
2
3
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4
5
6
7
8
Exercise 1 Solution
a.
3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7
• Constant
• Next number is 3.7
b.
2.5, 4.5, 6.5, 8.5, 10.5, 12.5, 14.5, 16.5
• 0.5 + 2x, where x specifies the position (index) of the number in the series
• Next number is 18.5
c.
5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5
• 4.5 + 0.5x + Cs, where x specifies the position (index) of the number in the
series
• Cs represents the seasonality factor
• C1 = 0, C2 = 2, C3 = 0, C4 = −2
• Next numbers: 9, 11.5, 10, 8.5
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Forecasting
Exercise 2
• Identify the pattern and construct a formula that will
“predict” successive numbers in the series.
• What is the next number in the series?
(a) 4.1, 3.3, 4.0, 3.8, 3.9, 3.4, 3.5, 3.7
(b) 2.9, 4.7, 6.8, 8.2, 10.3, 12.7, 14.2, 16.3
(c) 5.3, 7.2, 6.4, 4.5, 6.8, 9.7, 8.2, 6.3
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Exercise 2 Solution
• Same as series above, but with a random component
generated from normal random number generator with
mean 0
(a) 4.1, 3.3, 4.0, 3.8, 3.9, 3.4, 3.5, 3.7
• 3.7 +
(b) 2.9, 4.7, 6.8, 8.2, 10.3, 12.7, 14.2, 16.3
• 0.5 + 2x +
(c) 5.3, 7.2, 6.4, 4.5, 6.8, 9.7, 8.2, 6.3
• 4.5 + 0.5x + Cs +
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Forecasting Methods
• Qualitative methods
• Based on expert opinion
and intuition; often used
when there are no data available
• Quantitative methods
• Time series methods, causal methods
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Demand Behavior
• Trend
• Gradual, long-term up or down movement
• Cycle
• Up and down movement repeating over long time frame
• Seasonal pattern
• Periodic, repeating oscillation in demand
• Random movements follow no pattern
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Demand
Demand
Forms of Forecast Movement
Trend
Cycle
Random
movement
Time
Seasonal
pattern
Time
Demand
Demand
Time
Trend with
seasonal pattern
Time
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Forecasting
Averaging Methods
• Simple moving average
• Weighted moving average
• Exponential smoothing
• Averaging methods all assume that the variable of
interest is relatively constant over time; no trends or
cycles
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Simple Moving Average
Average over a given number of periods that is
updated by replacing the data in the oldest period
with that in the most recent period
F
+D +D
D
=
t −1
t
t −2
t −n
n
Ft = Forecasted demand for the period
Dt-1 = Actual demand in period t − 1
n = Number of periods in the moving average
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Weighted Moving Average
Simple moving average where weights are assigned
to each period in the average. The sum of all the
weights must equal one.
Ft = w D +w D
t −1
t −1
t −2
t −2
+ + w t − n Dt − n
Ft = Forecasted demand for the period
Dt-1 = Actual demand in period t − 1
wt-1 = Weight assigned to period t − 1
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Exponential Smoothing
Times series forecasting technique that does not
require large amounts of historical data
F = (1− )F
t
t −1
+ Dt −1
Ft = Exponentially smoothed forecast for period t
Ft-1 = Exponentially smoothed forecast for prior period
Dt-1 = Actual demand in the prior period
= Desired response rate, or smoothing constant
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Forecasting
Trend, Seasonal, and Cyclical Models
• Holt’s trend-adjusted exponential smoothing
technique
• Winter’s triple exponential smoothed model
• ARIMA models
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Holt’s Trend Adjusted Exponential Smoothing
Exponentially smoothed forecast that accounts for a trend in the data
FITt = Ft + Tt
and
Ft = αDt −1 + ( 1 − α)FITt −1
Tt = Tt −1 + δ(Ft −1 -FITt −1 )
FITt = Forecast for period t including the trend
Ft = Smoothed forecast for period t
Tt = Smoothed trend for period t
Dt−1 = Value in the previous period
0 = smoothing constant 1; 0 = smoothing constant 1
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Forecast Accuracy
• Error = Actual − Forecast
• Find a method that minimizes error
• Mean absolute deviation (MAD)
• Mean squared error
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Forecasting Steps
Model Development and Evaluation
• Identify purpose of forecast
• Determine time horizon of forecast
• Collect relevant data
• Plot data and identify pattern
• Select forecasting model(s)
• Make forecast
• Evaluate quality of forecast
• Adjust forecast and monitor results
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VVH Diaper Example
Week of Period Actual
1-Jan
8-Jan
15-Jan
22-Jan
29-Jan
5-Feb
12-Feb
19-Feb
26-Feb
5-Mar
12-Mar
19-Mar
26-Mar
1
2
3
4
5
6
7
8
9
10
11
12
13
70
42
63
52
56
53
66
61
45
54
53
43
60
Weekly Demand
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
Period
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10
11
12
13
VVH Simple Moving Average
F
F
+D +D
D
=
t −1
t
t −n
n
+D +D +D +D
D
=
13
14
t −2
12
11
10
9
5
60 + 43 + 53 + 54 + 45
= 51
F 14 =
5
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VVH Weighted Moving Average
F t = w D + w D ++ w D
F 14 = w D + w D + w D
F 14 = 0.5 60 + 0.3 43 + 0.2 53 = 53.5
t −1
t −1
t −2
t −2
13
13
12
12
t −n
11
t −n
11
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VVH Exponential Smoothing
F = D + (1 − ) F
F = D + (1 − ) F
F = (0.25 60) + (0.75 52) = 54
t
t −1
t −1
14
13
13
14
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VVH Comparison
(from the Excel template)
Weight 3
Weight 2
0.3
Periods
5
Least
Recent
0.2
MAD
MSE
7
86
MAD
MSE
6
75
Period Actual Forecast Error
1
70
2
42
3
63
4
52
5
56
6
53
57
4
7
66
53
13
8
61
58
3
9
45
58
13
10
54
56
2
11
53
56
3
12
43
56
13
13
60
51
9
14
51
Weight 1
Most
0.5 Recent
Period Actual Forecast Error
1
70
2
42
3
63
4
52
58
6
5
56
53
3
6
53
56
3
7
66
54
12
8
61
60
1
9
45
61
16
10
54
54
0
11
53
53
0
12
43
52
9
13
60
48
12
14
53.5
α
0.25
MAD
MSE
8
135
Period Actual Forecast Error
1
70
2
42
70
28
3
63
63
0
4
52
63
11
5
56
60
4
6
53
59
6
7
66
58
8
8
61
60
1
9
45
60
15
10
54
56
2
11
53
56
3
12
43
55
12
13
60
52
8
14
54
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Realities of Forecasting
• Forecasts are seldom perfect.
• Most forecasting methods assume
that there is some underlying
stability in the system.
• Service family and aggregated
service forecasts are more accurate
than individual service forecasts.
I see that you will
get an A this
semester.
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Order Amount and Timing
How much to hold
How much to order
When to order
• Basic economic order quantity (EOQ)
• Fixed order quantity with safety stock
• More models
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Definitions
Lead time—time between placing an order and receiving the order
Holding (or carrying) costs—costs associated with keeping goods in
storage
Ordering (or setup) costs—costs of ordering and receiving goods
Shortage costs—costs of not having something in inventory when it is
needed
Back orders—unfilled orders
Stockouts—occur when the desired good is not available
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Definitions
Independent demand is demand that is
generated by the customer and is not a
result of demand for another good or
service.
Dependent demand is demand that results
from another demand. Demand for tires and
steering wheels (dependent) is related to
the demand for cars (independent).
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Assumptions of the Basic EOQ Model
• Demand for the item in question is independent.
• Demand is known and constant.
• Lead time is known and constant.
• Ordering costs are known and constant.
• Back orders, stockouts, and quantity discounts are not
allowed.
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Inventory Order Cycle
Demand
rate
Order
quantity, Q
Average
amount of
inventory
held = Q/2
Inventory
Level
Reorder
point, R
0
Time
Lead
Lead
time
time
Order Order
Order Order
Placed Received Placed Received
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Reorder Point
The point in time by which stock must be ordered to
replenish inventory before a stockout occurs
R = dL
R = Reorder point
d = average demand per period
L = lead time (in the same units as above)
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EOQ Model Cost Curves
Minimum
Total Cost
Annual
cost ($)
Total Cost
Holding Cost = h*Q/2
Ordering Cost = o*D/Q
Optimal
Order Quantity
Q OPT =
2Do
2(Annual Demand)(Or der or Setup Cost)
=
h
Annual Holding Cost
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Order
Quantity, Q
EOQ Model Insights
• As holding costs increase, the optimal order quantity
decreases. (Order smaller amounts more often
because inventory is expensive to hold.)
• As ordering costs increase, the optimal order quantity
increases. (Order larger amounts less often because it
is expensive to order.)
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EOQ Model Implications
Total Cost
Annual
Cost ($)
Holding Cost
Ordering Cost
Q*
Q*
Order Quantity
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EOQ Model Implications
Total Cost
Annual
Cost ($)
Holding Cost
Ordering Cost
Q*
Q*
Order Quantity
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VVH Diaper Example
• Cost $5/case
• Holding costs 33% or $1.67/case-year
• Ordering costs $100
• Lead time 1 week
• She calculates annual demand as:
D = d period
= 53.5 cases of diapers
= 2,782 cases
week
52 weeks
year
year
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VVH Diaper Example
She calculates the reorder point as:
Reorder point = R = d L
= 53.5 cases
1 week = 53.5 cases
week
She calculates the EOQ as:
Economic order quantity = Q* =
=
2 o D
h
2 $100 2,782 cases
$1.67 case
= 333,174 cases 2 = 577 cases
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VVH Diaper Example
Annual demand
D=
Ordering cost per order (setup) S =
Annual carrying cost per unit
H=
Working days per year
=
Economic order quantity (EOQ)
=
Actual order quantity
Increment
Number of orders per year
Length of order cycle (days)
Average inventory
Annual carrying cost
Annual ordering cost
Total annual cost
2,782units/year
100 $/order
1.67 $/unit-year
365 days/year
577.21 units
Q = 577
DQ = 500
D/Q = 4.8 orders/year
Q/D = 75.7 days
Q/2 = 288.5 units
(Q/2) * H = $481.80
(D/Q) * S = $482.15
TC = $963.94
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Reorder Point with Safety Stock
Order
quantity (Q)
Inventory
level
Reorder
point (R)
Safety
stock (SS)
0
Lead
time
Time
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Lead
time
Reorder Point with Safety Stock
Reorder point
Safety stock
R = d L + SS
SS = z L
where
z is the z-score associated with the desired service level
(number of standard deviations above the mean)
L= standard deviation of demand during lead time
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Normal(100, 20)
2.5
Safety Stock
2.0
Normal(100, 20)
2.5
1.5
Probability of
meeting demand during
lead time = service level = 84%
BestFit Student Version
2.0
For Academic Use Only
Reorder point
1.0
1.5
BestFit Student Version
0.5
For Academic Use Only
0.5
120.0
120
100
100
120
160
84.1%
140
120.0
Average demand during
Lead time = dL
Z
0
1
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Model Insights
• As the desired service level increases, the amount of
safety stock increases. (If fewer stockouts are desired,
more inventory must be carried.)
• As the variation in demand during lead time
increases, the amount of safety stock increases. (If
demand variation or lead time can be decreased, less
safety stock is needed.)
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VVH Diaper Example
• Desired service level = 95 percent
• With five orders/year, this means that the hospital would experience
one stockout every four years
• Standard deviation of demand during lead = σL = 11.5 cases of
diapers
• Amount of safety stock needed:
SS = z L = 1.64 11.5 = 18.9 cases
• New reorder point:
(
R = d L + SS = 53.5 cases
week
)
1 week + 18.9 cases = 72.4 cases
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VVH Diaper Example
Average daily
demand
d=
7.64
units
Average lead time
L=
7
days
Std dev demand during lead
time
L =
11.5
units
Service level
SL =
0.95
Increment
DSL =
0.05
z associated with service level
1.64
Average demand during lead
time
Safety
stock
Reorder point
dL =
53.48
Probability
Stock out risk
Reorder Point
units
0.0
20.0
40.0
60.0
Daliy Demand
daily demand
SS =
18.9
units
ROP =
72.4
units
80.0
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ROP
100.0
VVH Diaper Example
Average demand =
53.5 cases/week
Order
quantity (577)
Inventory
level
Safety
stock (19)
Reorder
point (72)
0
Lead
time =
1 week
Time
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Lead
time
Additional Inventory Models
• Fixed period with safety stock
• Orders are bundled and/or vendors deliver according to a
set schedule
• Quantity discounts
• Price breaks
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Inventory Systems
• Two-Bin
• JIT
• MRP
• ERP
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Two-Bin System
When the first bin is empty,
stock is taken from the second
bin and an order is placed.
There should be enough stock
in the second bin to last until
more stock is delivered.
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JIT—Kanbans
Empty
Kanban
Empty
Kanban
Full
Kanban
Full
Kanban
Task 1
Workstation
1
Task 2
Workstation
2
Customer
Order
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Flow and Pull
• Continuous or single piece flow—move items (jobs,
patients, products) through the steps of the process one
at a time without interruptions or waiting.
• Pull or just-in-time (JIT)—products or services are not
produced until the downstream customer demands them.
• Heijunka (i.e., “make flat and level”)—eliminate variation
in volume and variety of production.
• Level patient demand
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MRP Product Structure
Table
(end item)
Lead time = 1 week
Tabletop
(1)
Lead time = 2 weeks
Leg
(4)
Lead time = 3 weeks
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MRP Logic
Order
tabletops
Order
table legs
Week
1
2
3
4
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5
ERP Systems Link Functional Areas
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Procurement and Vendor
Relationship Management
• E-procurement
• Value-based standardization
• Outsourcing
• Vendor managed inventory (VMI)
• Automated supply carts
• Group purchasing organizations (GPO)
• Disintermediation
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Strategic Supply Chain Management
Many are the same as any other improvement/change initiative:
• Top management support
• Employee buy-in
• Structure and staffing need to support the desired
improvements
• Process analysis and improvement
• Need relevant, accurate data and metrics
• Training
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Strategic Supply Chain Management
• Need to evaluate cost and benefits of technologyenabled solutions
• Need to highlight the necessity and benefits of
strategic supply chain management
• Improved inventory management through better
understanding of the systems
• Consequences of unofficial inventory
• Just-in-time systems
• Improved inventory tracking systems
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Strategic Supply Chain Management
• Vendor partnerships
• Information sharing
• Investigation and determination of mutually beneficial
solutions
• Performance tracking
• Continually educate and support a systemwide view
of the supply chain and seek improvement for the
system rather than for individual departments or
organizations in that system.
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End of Chapter 13
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Chapter 14
Improving Financial Performance with
Operations Management
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Factors Influencing
Financial Performance
• Increasing incidence of chronic disease and an aging
population
• New diagnostic and treatment technologies
• Movement from inpatient to outpatient and home care
• Increasing complexity of billing and payment systems
• A provider payment system (fee-for-service) that encourages
the use of healthcare services
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Measures of Financial Performance
• Cash on hand
• Percent of debt financed
• Age of plant
• Revenue (growth or decline)
• Profit margin
• Costs (per unit of service)
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Short-Term and Ineffective
Approaches to Cost Reduction
• Across-the-board expense reductions
• Elimination of overtime without changing any processes
• Using less expensive supplies without changes in the supply
chain
• Tolerating queuing and long waits for service
• Outsourcing key activities without quality monitoring systems
in place
• Implementing automation without a clear, positive financial
impact
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Systems Approach to Cost Reduction
• Identify expenses directly related to revenue and those
that are overhead
• Revenue categories:
• Fee-for-service
• Bundled
• Shared savings
• Full capitation
• Quality bonuses or penalties
• Global budgets
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Cost Reduction Approach
• Collect detailed data
• Identify variances in resources used and outcomes
achieved
• Improvement methods
• Process improvement including Lean and Six Sigma
• Supply chain management
• Schedule optimization
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Fee for Service
• Lowest “atomic” level of costs
• Use activity-based costing
• Identify the relevant activities
• Determine the total cost of each activity, including direct
and indirect costs
• Determine the cost drivers for the activity
• Collect activity data for each service
• Calculate the total cost of the service by aggregating
activity costs
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ABC – Step 1
Activity
Annual
Cost
Cost Driver
Service A
Activity
Data
Service B
Activity
Data
Total
Activity
Data
Check-in
$ 50,000
Assessment
Allocation
Rate
Number of Visits
5,000
5,000
10,000
$5.00
75,000
Number of
minutes per visit
5
10
75,000
1.00
Diagnosis
250,000
Number of
minutes per visit
10
15
125,000
2.00
Treatment
450,000
Number of
minutes per visit
10
20
150,000
3.00
Prescription
2,500
Drugs prescribed
per visits
0.5
2.0
12,500
0.20
Checkout
50,000
Number of visits
5,000
5,000
10,000
5.00
Billing
150,000
Number of bills
per visit
1.0
2.0
15,000
10.00
Total Costs
$1,027,500
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ABC – Step 2
Activity
Cost Driver
Check-in
Number of Visits
$5.00
1
$5.00
1
$5.00
Assessment
Number of
minutes per visit
1.00
5
5.00
10
10.00
Diagnosis
Number of
minutes per visit
2.00
10
20.00
15
30.00
Treatment
Number of
minutes per visit
3.00
10
30.00
20
60.00
Prescription
Drugs prescribed
per visits
0.20
0.5
0.10
2.0
0.40
Checkout
Number of visits
5.00
1
5.00
1
5.00
Billing
Number of bills
per visit
10.00
1.0
10.00
2.0
20.00
Total Cost
per service
Rate
Service A
Consumption
Service A Service A
Cost
Consumption
Service B
Cost
$75.10
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$130.40
Cost Reduction Tools for Each Activity
Activity
Improvement tools
Opportunity
Check- in
Process improvement (Lean and Strong
Six Sigma, Simulation, etc.)
Automation
Assessment
Process Improvement
Low
Diagnosis
Evidence-based medicine
Medium
Treatment
Evidence-based medicine
Medium
Prescription
Supply chain management
Strong
Checkout
Process improvement,
Strong
Automation
Billing
Data mining and analysis,
Strong
Process improvement
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Bundled Payment Types
• Per diem. All payments for a day in a hospital are paid at
one rate.
• Medicare prospective payment. All payments for a stay in
the hospital are paid at one rate that is adjusted for the
complexity of the admission by the diagnosis-related
group system.
• Medicare bundled payments. All payments for an “episode
of care” are paid at one rate adjusted for complexity.
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Cost Reduction for Bundled Payments
• Identify bundles with:
• High volume
• High cost compared to benchmarks from other organizations
• High use in bundled payments where costs are highly variable
• Reduce costs of FFS components
• Use evidence-based medicine to identify and use most
effective clinical protocols
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Shared Savings
ACO Cost Management
• Data tools and advanced analytics to track patients over time
• Six Sigma analysis and improvement
• Run and control charts
• Pareto diagrams
• Cause-and-effect diagrams
• Scatter plots
• Regression analysis
• Benchmarking
• Chronic disease management using evidence-based medicine
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Full Capitation Cost Control
• Disciplined attention to improving systems of care
• Additional tools:
• Implement healthcare home
• Implement shared decision making for surgery based on
EBM findings
• Develop new systems to prevent readmissions of Medicare
patients through EBM and process improvement
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Global Payment
Annual Budget Cost Management Tools
• Balanced scorecard strategy maps and reporting
• Analytics, benchmarking, and statistical tools to identify
opportunities for cost reductions
• Process improvement with Lean and Six Sigma with a special
emphasis on services that are developing queues
• Scheduling and capacity management
• Supply chain management
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Controlling Overhead Expenses
• Implement PI on all routine overhead processes (e.g.,
hiring new employees)
• Consolidate activities (e.g., memberships)
• Reduce staffing layers
• Reduce meetings – use automation tools
• Focus on capital cost reduction and effective space
use
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Prioritize Overhead Activities
Function B
Function C
Importance
Function A
Function D
Cost
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End of Chapter 14
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Chapter 15
Emerging Trends in Healthcare
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Introduction
• Advancements in technology and emergence of new care delivery
models hold the potential to make substantial changes to healthcare
delivery.
• We discuss the following in this chapter:
▪ Patient-centered care
▪ Blockchain and decentralized applications
▪ Virtual care
▪ Home health
▪ Care providers’ involvement in population health
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Patient-Centered Care
“providing care that is respectful of, and responsive to, individual patient
preferences, needs and values, and ensuring that patient values guide all
clinical decisions”
Key dimensions:
• Respect for patient preferences
• Coordination and integration of care
• Information and education
• Patient comfort
• Emotional support
• Involvement of family and friends
• Continuity and transition
• Access to care
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Patient-Centered Care
Traditional Care Delivery Model
Disease-centric approach.
Patient-Centered Care Delivery Model
Holistic and integrated care delivery aimed at
improving the overall health of the patient.
Patient’s Role
Patient plays a passive role.
Patient plays an active role in their care.
Care Provider’s Role
Primary determinant of the treatment
plan based on best generalized clinical
guidelines available.
Healthcare
Organization’s Role
Lower need for coordination of care.
Collaborates with the patient to determine a
customized treatment plan that accounts for
the best available clinical guidelines and
patient needs.
Higher need for coordination of care.
Family Participation
Family members play a passive role in
patient’s care delivery.
Outcomes
Potential lower adherence to treatment
plan.
Care Delivery
Philosophy
Family participation in developing patient’s
treatment plan and family caregiving is
encouraged.
Patient has a better understanding and
higher likelihood of adherence to their
treatment plan, resulting in improved clinical
outcomes and quality of life.
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Blockchain and Decentralized Applications
• Blockchain is a decentralized distributed ledger.
• Decentralized applications (DApps) are software solutions
that rely on blockchains for data storage.
• Key benefits of Dapps over traditional applications include:
• Improved interoperability
• Improved data security
• Cost-effective data access
• Patient data ownership
• Improved data privacy
• Smart contracts
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Virtual Care
“Virtual care refers to a holistic and comprehensive system of
technology-enabled care delivery, which removes constraints
on the location and time of care.”
• Virtual care relies on software solutions to provide a comprehensive set
of care delivery services to patients.
• Care delivery services include virtual consultations, asynchronous visits,
and remote monitoring and adherence tracking.
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Virtual Care
Source: Exhibit from “Virtual health: A Look at the Next Frontier of Care Delivery,” June 2020, McKinsey & Company, www.mckinsey.com. Copyright © 2021 McKinsey & Company. All rights
reserved. Reprinted by permission.
Copyright © 2022 Foundation of the American College of Healthcare Executives.
Not for sale.
Home Health
• Home health involves in-person interactions between the care providers
and patients in their home setting.
• Recent technological advancements, improvements in home health
reimbursement rates, and legislative actions have improved home
health volumes.
• Home health interactions can be of varying levels of intensity
• Episodic care
• Long-term assistance services
• Rehabilitative services
• Transition care
• Nonclinical care
Copyright © 2022 Foundation of the American College of Healthcare Executives.
Not for sale.
Population Health
• Hospitals are increasing their involvement in community health
initiatives.
• This enables hospitals to influence social and behavioral factors that
may impact health, enabling a holistic approach to improving patient
health.
• Approaches to community involvement include:
• Coordination of care
• Collaborations with community employers
• Collaborations with community service organizations
• Collaborations with government agencies
Copyright © 2022 Foundation of the American College of Healthcare Executives.
Not for sale.
Other Advancements
• Artificial intelligence and
machine learning
• Digital therapeutics
• Internet of medical things
• Virtual and augmented
reality
• Computer vision and image
processing
• Facial recognition
• Medical chatbots
• Robotic process automation
• Cobots
• 3D printing
• Autonomous vehicles and
drones
Copyright © 2022 Foundation of the American College of Healthcare Executives.
Not for sale.
End of Chapter 15
Copyright © 2022 Foundation of the American College of Healthcare Executives.
Not for sale.
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