Download Analysis of the medication management system in

Analysis of the medication
management system in
seven hospitals
James Baker, Clinical Director, Marketing, Medication Technologies, Cardinal Health
Marcy Draves, Clinical Director, Marketing, Medication Technologies, CareFusion
Amar Ramudhin, École de Technologie Supérieure, 1100 Notre Dame West, Montreal, H3C1K3
Summary
This paper examines how the various medication dispensing pathways used in seven hospitals influence patient
safety and efficiency of medication workflow. Specifically, it considers how different dispensing pathways
impact the rapid initiation of medication therapy, the predictability of medication availability on patient care
areas, the frequency and effects of missing medications and workload. Patient medication safety includes rapid
initiation of medication orders and adherence to the prescribed medication regimens. Our findings suggest the
safest medication management system to achieve these metrics is one in which an increased percentage of
medications are managed through the automated dispensing cabinet (ADC) pathway.
Introduction
A new medication is prescribed for a patient. This action sets in motion a complex series of interrelated supply
chain and workflow processes aimed at providing the patient this medication as quickly, efficiently, accurately and
cost-effectively as possible. However, within hospitals and across the healthcare system, these processes vary in ways
that may have consequences. Variations impact initiation and timing of medication therapy for patients. They also create
pressure, stress and inefficiency for pharmacists and nurses, and workflow disruptions. No other interdepartmental
relationship within a hospital demands this many daily synchronization points. No other interdepartmental relationship
or synchronization is as critical if not done or done incorrectly. A breakdown at any point in these processes may lead to
delayed, omitted or incorrect medication therapy.
The systems employed in distributing and administering medications within hospitals are developed primarily to
promote medication safety. Yet these systems are also built to accommodate either nursing or pharmacy workflow.
Furthermore, it is conjectured that systems employed to favor one of these professions are counterproductive to
the other. The question to be answered is: what is the safest, most effective medication management system for
the hospital and ultimately the patient?
1
For hospitals to reduce patient morbidity and mortality, it is imperative that they initiate appropriate medication therapy
rapidly and continue to administer therapy as scheduled. Patients have a right to receive and hospitals have an obligation
to provide, the right medication, for the right reason, at the right time. For this to occur, nurses need the correct medications
available at the time they plan to administer them. And before these medications can be dispensed, pharmacists are
required by regulations and professional standards to review medication orders for patients while also assuring the
medications are secure, yet available for the nurse when needed.
Several factors can complicate this workflow and impact the timely delivery of medications. The sheer volume of new
medication orders exacerbates the challenge. Today, within a 300-bed hospital, nurses may administer 2,400 to 4,000
doses of medications daily across all patient care areas. Therefore, pharmacy, nursing and technological processes must
all be synchronized to provide the correct medication, supplies and information for each one of these transactions.
Patient transfers between patient care areas complicate this synchronization effort. Further, hospital pharmacies use various
medication dispensing and delivery pathways, which depend on the type of medication prescribed and the extent and type
of dispensing automation employed. For nursing, the type of medication and the dispensing automation employed impact
the timeliness of availability.
As medications are one of the primary modes of treatment and errors may cause patient harm, there have been a
plethora of technological advances focused on improving the safety and efficiency of these processes. Technology
is now available to assist with all the major medication processes: prescribing, dispensing, administering, documenting
and monitoring medication therapy. However, as such recent technologies as computerized prescriber order entry (CPOE),
barcode medication administration (BCMA) and electronic health records (EHR) are implemented, hospital leaders are
concerned as to how these technologies are impacting or are impacted by those workflow processes developed to support
previously implemented dispensing technologies. This requires a thorough understanding of the complex interactions
between people, materials and information.
Methodology for analysis of system complexities
To comprehend the complex interactions of pharmacy and nursing processes involved with medication and information
flow in acute care hospitals, we engaged with our partner Bluesail Solutions and deployed technology specifically designed
to model, visualize, communicate and analyze complex healthcare processes. This approach included the use of medBPM®
software (medical Business Process Modeling) and modeled all the activities and variations involving patients, healthcare
professionals, paper and electronic information and materials (medications and supplies).
The complexity and multi-dimensional nature of healthcare processes make it especially difficult to map these processes
using commercially available modeling tools. The medBPM tool was developed specifically for the healthcare domain
to capture the many activities of specialists, clinical professionals, information systems, equipment and materials in a
straightforward fashion that is easy for clinicians to understand.
2
A side-by-side study of six technologies used to model medical processes recently concluded the medBPM approach was
best suited to model the medication ordering and preparation process because it focused not only on the activities that
make up a process but on the resources that make up activities.1 An example of the breadth and depth of medBPM
modeling is depicted in Figure 1.
Figure 1: Sample of medBPM process
Designated
med storage
Pathway/KPIs
Travel
5
Med room
3
Medication
Triggered by 4
Location
Metrics
Information
2
MedAdmin 00:02:00 $0.44
Med room
MedAdmin 00:03:00 $0.66
Med room
Patient room
4 Go to patient room
Find medication
200ft
Mean Time = 00:02:00
Std. Dev. Time = 00:00:00
Cost=0.44
Mean Time = 00:01:00
Std. Dev. Time = 00:00:24
Cost=0.22
MAR
Medication
2
MedAdmin 00:08:00 $1.76
Patient room
6
Administer
medication
Mean Time = 00:05:00
Std. Dev. Time = 00:01:15
Cost=1.10
Provider
Provider
Medication
2
2
Triggered by
Scheduled Med
1
7
Patient/Provider
Patient/Provider
Interaction
Interaction
Medication
Medication
8
Figure 1 illustrates the various components analyzed including activities, resources, wait queues, travels, communications,
interactions, information flow, integration points, locations, human resource utilization, time, cost, distance and other
key process indicators (KPIs).
Using the medBPM software, the medication management processes were analyzed in seven hospitals. The methodology
included pre-visit nursing and pharmacy questionnaires, on-site interviews, observations, measurements and modeling of
pharmacy and nursing pathways by a multidisciplinary team. Final models and reports were prepared off-site and shared
with each hospital. The acceptance of the results by their respective leadership validated the accuracy of the analysis and
the models.
3
Table 1 depicts key parameters for each of the seven hospitals and illustrates their differences relative to daily census,
volume of medication orders, deployment of various technologies and the number and type of patient care areas studied.
Table 1: Hospital demographics
Hospital statistics
1
2
3
4
5
6
7
% of doses in profiled ADCs
4%
23%
25%
66%
76%
80%
95%
Average daily census
616
212
300
344
225
100
425
3,500
1,596
1,600
2,900
2,186
N/A
4,222
CPOE
yes
no
no
no
no
no
yes
Electronic Medication Administration Record
yes
yes
yes
yes
no
yes
no
BCMA
no
yes
yes
no
no
yes
no
Carousels
no
no
no
3
no
no
no
Centralized robot
yes
yes
yes
no
no
no
no
ADC wholesaler restocking
program
no
no
no
yes
yes
no
no
Barcode ADC replenishment system
no
no
no
no
no
yes
yes
Medical surgical unit
2
1
1
1
—
1
1
Critical care unit
—
1
1
1
1
1
1
Average number of daily medication orders
Technology
Patient care areas studied
The analysis of each and the comparisons across the seven hospitals provided an understanding of the complexity and
the variations of the medication management system. The data also supported conclusions about what is the safest,
most effective medication management system for the hospital and, ultimately, the patient.
Influence of pathways on rapid initiation of new medication orders
Components of Time to Initial Dose
Rapid initiation of medication therapy has become increasingly important as patient acuity increases and hospitals
strive to reduce patient length of stays. This analysis uses Total Time to Initial Dose (TTID) to measure how rapidly
hospitals initiated therapy. TTID is defined as the period of time starting when the medication was prescribed and
concluding after the initial dose was administered and documented. TTID encompasses the following processes:
1. Order prescription and transmission to pharmacy
2. Order review and entry in the pharmacy information system
3. Medication preparation, verification and delivery
4. Medication administration and documentation
4
Our findings demonstrate that the most significant factor influencing TTID is the percentage of a hospital’s medications
dispensed through automated dispensing cabinets (ADCs). The more medications dispensed through this route, the faster
the weighted average TTID. Figure 2 illustrates the relationship between the weighted average TTID and the percent of
medications dispensed through ADCs.
Figure 2: Weighted average total time to initial dose (TTID)
1:40:48
TTID
h:mm:ss
1:26:24
Poly.(TTID)
1:12:00
0:57:36
0:43:12
0:28:48
Y=-0.0005x3 + 0.0037x 2 - 0.0072x + 0.0512
0:14:24
0:00:00
R2 = 0.7472
4%
23%
25%
66%
76%
80%
95%
% Doses in ADC
The weighted average was determined by the percent of medications dispensed through each of the pharmacy’s major
medication dispensing pathways: ADCs, sterile preparations and unit-dose (Table 2).
Table 2: weighted average total time to initial dose (ttid)
Weighted average time to initial dose
Hospitals
% Initial doses in ADC
TTID for all medications
1
2
3
4
5
6
7
4%
23%
25%
66%
76%
80%
95%
1:14:02
0:51:52
1:24:00
1:18:23
1:08:29
0:49:36
0:31:26
TTID by medication dispensing pathway
ADC
0:31:26
0:27:15
0:39:00
1:12:43
0:48:01
0:33:00
0:25:24
Sterile preparation
1:20:32
0:59:39
1:51:41
1:38:57
1:45:37
2:05:19
1:14:27
Unit dose
1:12:26
1:03:38
1:43:00
2:14:40
1:43:21
1:32:00
1:10:27
The first component of the TTID was defined as the duration of time from when the order is prescribed until it is received
in the pharmacy. Each of the seven hospitals employed one or more electronic systems for transferring physician
medication orders from the patient care areas to the pharmacy (CPOE, scanners, fax). The average duration ranged
from 91 seconds to 17 minutes and was primarily influenced by two factors: the percentage of orders prescribed with
CPOE and the awareness of the new medication orders on the patient care areas.
5
The second component, the time to review and enter orders in the pharmacy information system, ranged from 0:13:50
to 0:54:00. Two order review and entry models were observed—one that utilized only pharmacists and the other with
technician order entry followed by pharmacist verification. The six hospitals that used the first model had processing
times of 25 minutes or less, while the hospital that used technician order entry with pharmacist verification had a
processing time of 54 minutes.
The third component of TTID included the time required for medication preparation, verification and delivery for
each of the three dispensing pathways. Table 3 shows the average time required for each of the pathways. Times
for the ADC pathway were 0:0:00 because there was no preparation, verification and delivery required.
Table 3: Medication preparation, verification and delivery
Medication preparation, verification and delivery
Dispensing pathways by hospital
1
2
3
4
5
6
7
ADC
0:00:00
0:00:00
0:00:00
0:00:00
0:00:00
0:00:00
0:00:00
Sterile preparation
0:48:00
0:33:02
1:10:10
0:25:10
1:03:34
1:30:41
0:40:12
Unit dose
0:41:16
0:36:55
0:48:41
1:02:44
1:21:30
0:58:22
0:47:04
The fourth component included the time required for medication administration and documentation. Separate nursing
workflows were modeled for oral, injectable, patient-specific and intravenous medications. The workflow started from the
time the nurse was aware a medication was due until administration and documentation were completed. Twelve patient
care areas were analyzed. The patient care area from hospital 5 was excluded due to insufficient data.
Table 4 shows the medication administration times by care areas. It illustrates that administration times were less in critical
care units than in medical surgical units and administration times were longer for medications requiring preparation. The
average time to administer the four medication types ranged from 0:01:27 to 0:05:00.
Table 4: Medication administration times
Administration times for medical surgical (MS) and critical care (CC) units
Hospital
Patient care area
Oral
1
2
3
MS
MS
MS
CC
0:01:47
0:01:47
0:01:37
0:01:37
MS
4
CC
MS
6
CC
MS
7
CC
MS
CC
0:03:20
0:02:36
0:02:47
0:02:21
0:02:00 0:03:43
0:03:15
0:04:17 0:00:44 0:02:06 0:02:00 0:03:05 0:00:51
Patient specific
0:01:29
0:01:29
0:01:21
0:01:20 0:04:18 0:00:45
0:01:36
0:01:42
0:03:06 0:00:51
Injectable
0:02:01
0:02:01
0:01:58
0:01:53
0:05:20
0:01:49
0:02:36
0:02:30
0:04:10
Intravenous infusion (IV)
0:02:28
0:02:28
0:02:23
0:02:15
0:06:05 0:02:32
0:03:33
0:03:27
0:04:53 0:02:38 0:04:06 0:03:32
Average
0:01:56
0:01:56
0:01:50
0:01:46 0:05:00
0:02:28
0:02:25
0:03:49
0:01:27
0:01:35
0:03:29 0:02:56
6
Six patient care areas had an average administration time of less then 0:02:00 (Figure 3). In these care areas, the increased
efficiency was due to the close proximity to the patient of medication information, patient information, medication, supplies
and equipment.
Figure 3: Average administration times
h:mm:ss
0:06:00
MS2
0:05:00
MS1
0:04:00
CC
0:03:00
0:02:00
0:01:00
0:00:0 0
1
2
3
4
6
7
Hospitals
The medical surgical units of hospitals 3 and 6 had the longest administration times due to the need to move the
computer on wheels over a significant distance. Both units in hospital 7 had increased administration times as the
nurse was required to locate a manual medication administration record (Table 5).
Table 5: Primary factors influencing administration times
Primary factors influencing administration times
Hospital
Patient care area
1
2
3
4
6
7
MS1
MS2
MS1
CC
MS1
CC
MS1
CC
MS1
CC
MS1
CC
Number of medication
locations for scheduled doses
3
3
4
2
2
2
4
4
2
1
2
1
Number of medication
locations for initial doses
5
5
3
3
2
2
3
4
3
2
2
1
Walking time from primary
medication location to
patient (h:mm:ss)
0:00:04 0:00:04 0:00:04 0:00:08 0:00:00 0:00:05 0:00:16 0:00:11 0:00:07 0:00:04 0:00:15 0:00:06
Walking time from medication
information to patient (h:mm:ss)
0:00:04 0:00:04 0:00:04 0:00:02 0:00:42 0:00:00 0:00:05 0:00:02 0:00:32 0:00:00 0:00:20 0:00:16
7
TTID, predictability and non-value added steps
On the patient care areas, the most challenging aspect of administering an initial dose is the lack of predictability of
medication availability. This causes a significant amount of frustration for nurses. Predictability is usually impacted by
the variability within a process. To understand the causes of this variability, we further analyzed the detailed models
for each pathway and identified significant differences in both the number of process steps and the number of
non-value added (NVA) steps.
Table 6 shows the minimum attainable number of NVA steps by medication dispensing pathway.
Table 6: Minimum attainable number of non-value added steps by medication dispensing pathway
Non-value added process
Wait queue for medication order entry
ADC pathway
Unit dose pathway
1
1
Wait queue for medication preparation
1
1
Wait queue for medication verification
1
1
Wait queue for medication delivery
1
1
4
4
Total
1
Sterile preparation
pathway
1
The ADC dispensing pathway has only one non-value added step: the order wait time before pharmacist processing begins.
Once the medication order has been processed, the medication is available on the patient care area. The more medications
dispensed through this route, the faster the weighted average TTID.
The dispensing routes for sterile preparations and unit dose medications introduce additional non-value added steps.
These steps include:
1. Wait for preparation
2. Wait for verification
3. Wait for medication delivery
Each additional non-value added step increases time for the medication to be available on the patient care area.
8
Table 7: Actual number of NVA steps by medication dispensing pathway
Hospital
% Doses in ADCs
1
2
3
4
5
6
7
4%
23%
25%
66%
76%
80%
95%
NVA TTID NVA TTID NVA TTID NVA TTID NVA TTID NVA TTID NVA TTID
steps h:mm:ss steps h:mm:ss steps h:mm:ss steps h:mm:ss steps h:mm:ss steps h:mm:ss steps h:mm:ss
ADC pathway
1
0:31:26
1
0:27:15
1
0:39:00
3
1:12:43
1
0:48:01
1
0:33:00
1
0:25:24
Unit dose pathway
6
1:12:26
4
1:03:38
4.5
1:43:00
11
2:14:40
5
1:43:21
7
1:32:00
4
1:10:27
Sterile preparation
pathway
7
1:20:32
4
0:59:39
5
1:51:41
8
1:38:57
5
1:45:37
6
2:05:19
4
1:14:27
Weighted average
TTID
1:14:02
0:51:52
1:24:00
1:18:23
1:08:29
0:49:36
0:31:26
Table 7 shows the actual number of NVA activities and TTID by dispensing pathway in each of the seven hospitals.
Hospitals 2 and 7 were the most efficient due to the minimum number of NVA steps in all dispensing pathways.
However, hospital 7 had the lowest TTID (0:31:36) due to the highest percentage of medications managed through
the ADC pathway (95%). The lack of predictability for nurses was limited to the 5% of the initial doses that flow
through the unit dose and sterile preparation pathway.
In contrast, for hospital 1, 96% of initial doses were unpredictable for nurses due to the extra NVA steps and the low
percentage of medication managed through the ADC pathway (4%). Although hospital 4 had 66% of medications
managed through ADCs, its TTID (1:18:23) was high and its predictability low due to the high number of NVA steps.
In addition to the NVA, wait queues that exist in the unit dose and sterile preparation pathways, extra process steps
are required for medication preparation, verification and delivery. These waits and process steps not only result in delay,
but also introduce higher variability in TTID. For those medications in these two pathways that could be managed through
the ADC pathway, the NVA steps and the extra process steps could be eliminated, along with the potential for variability.
An additional element of predictability was the location for medication delivery. Initial doses managed through the unit
dose or sterile preparation pathways were delivered to more than one location. Nurses had to guess when and where to
look for these medications.
The following observations were concluded from the TTID analysis: the higher the percentage of medications available
in the ADC pathway, the lower the TTID, the lower the number of process and NVA steps and the higher the predictability
of medication availability for nurses.
Influence of pathways on missing doses
A significant effort in nursing is the continuation of medication therapy after the initial dose. Approximately 80%
of total doses administered are to continue medication therapy. Similar to initial doses, the efficiency of the medication
administration workflow was dependent upon the close proximity of medication information, patient information,
medication, supplies and equipment to each other and also to the patient.
However, our findings suggest that although the distance from the patients of these essential items was important
for efficiency, the more negative impact to patients and the greatest frustration to nurses was when they were unable
to complete their plan of care if one of these items was not available.
9
Nurses defined a missing medication as any medication that could not be located after a perceived diligent search.
In hospitals 1 and 2, which had the lowest percentages of medications managed through ADCs, the nurses reported
that there was greater than one missing medication per patient per day.
In contrast, in hospitals 6 and 7, where the majority of the medications were managed through ADCs, the nurses
reported the incidence per patient day as minimal.
Table 8 shows the data on missing medications received from five hospitals. In hospitals 1 and 2, additional pathways
were developed to model nursing activity in the event of missing doses.
Table 8: Reported incidence and impact of missing doses
Incidence and impact of missing doses
Hospital
1
2
3
6
7
4%
23%
25%
80%
95%
Pharmacy estimate
0.65
0.24
0.25
0.03
<.03
Nursing estimate
1.72
1.17
n/a
minimal
minimal
Percent of doses in ADC
Missing doses per patient per day
5.73
6.47
n/a
n/a
n/a
Nursing additional time per missing dose
Nursing non-value added (NVA) steps per missing dose
0:02:02
0:01:05
0:02:07
0:01:18
0:03:05
Weighed average for time to administer missing meds
1:19:31
0:31:05
n/a
n/a
n/a
Pharmacy time per missing dose per patient day
0:03:24
0:01:22
0:04:44
0:00:25
n/a
The missing dose pathways developed in these hospitals revealed that nursing had between 5.73 and 6.47 NVA steps
per missing dose and a delay in administration time of 1:19:31 and 0:31:05.
Pharmacists defined missing doses as those medications in which duplicate doses were delivered. Additional labor time
for duplicate work ranged from 0:00:25 to 0:03:21 per patient per day.
In the hospitals in which the majority of the medications were available via an ADC, the pharmacy and nursing estimates
were consistent. In contrast, for the hospitals that had the least amount of medications available in ADCs, the incidence
reported varied between pharmacy and nursing.
Influence of pathways on pharmacy workload
Workload computation
After initial medication doses are processed and administered, the pharmacy delivers subsequent doses required to
continue therapy to the patient care areas daily. Unlike initial dose dispensing activities, these subsequent medication
doses are planned activities. All hospitals in the study used both their pharmacy information system and the inventory
management software features of their ADCs to schedule and coordinate the replenishment processes. In addition,
several hospitals further supported replenishment processes with automation and/or outsourced services.
10
The following table depicts the type and purpose of the supportive automation products or services used in the
study hospitals (Table 9).
Table 9: Type and purpose of product/service employed to support daily medication replenishment activities
Hospital
Product/service
Primary purpose
1
Dispensing robot
Improve med cassette fill productivity and accuracy
2
Dispensing robot
Improve med cassette fill productivity and accuracy
3
Dispensing robot
Improve med cassette fill productivity and accuracy
4
Carousel, ADC wholesaler restocking program
Improve ADC refill productivity and accuracy
5
ADC wholesaler restocking program
Improve ADC refill productivity and accuracy
6
Barcode ADC replenishment system
Improve ADC refill productivity and accuracy
7
Barcode ADC replenishment system
Improve ADC refill productivity and accuracy
In managing both initial and replenishment doses, total pharmacy labor required for pharmacy dispensing services varies
depending on both the percentages of medications managed through each of the three pathways and the extent to
which these pathways are supported by other products and services.
Table 10 shows the pharmacist and pharmacy technician time required per day for the preparation and verification of
11
initial doses and for each hospital’s medication replenishment process. Pharmacist order review and entry labor was
not included in the workload calculations because it is a required process for all medications independent of the
pathways. Delivery workload for initial doses was also omitted in the calculation of initial dose dispensing. Hospital 5
was not included in this analysis due to insufficient data.
Table 10: Pharmacy labor for preparation, verification and replenishment
Workload normalization
Hospital
1
2
3*
4**
6*
7*
Percent of total doses in ADC
4%
23%
25%
66%
80%
95%
Average daily census
616
212
300
344
100
425
Average number of daily orders
3,500
1,596
1,600
2,900
723
7,222
Average number of doses
dispensed daily
16,438
4,700
7,562
8,184
1,808
7,086
Number of orders per patient day
5.7
7.5
5.3
8.4
7.2
9.9
Number doses per patient day
26.7
22.2
25.2
23.8
18.1
16.7
Initial dose preparation and verification labor (excludes delivery)
Pharmacist
13:07:30
2:19:54
8:20:00
16:12:09
0:52:26
2:57:41
Technician
44:08:20
35:30:56
32:52:00
13:47:30
11:25:19
11:01:27
Pharmacist
25:40:08
8:18:10
3:41:18
5:46:26
0:50:25
5:56:30
Technician
192:00:00
29:13:04
38:16:55
38:56:28
3:48:35
29:53:40
Pharmacist
38:47:38
10:38:04
2:01:18
21:58:35
1:42:51
8:54:11
Technician
236:08:20
64:44:00
71:08:55
52:43:58
15:13:54
40:55:07
Pharmacist
0:03:47
0:03:01
0:02:24
0:03:50
0:01:02
0:01:15
Technician
0:23:00
0:18:19
0:14:14
0:09:12
0:09:08
0:05:47
Total labor per patient day
0:26:47
0:21:20
0:16:38
0:13:02
0:10:10
0:07:02
Medications replenishment labor
Total labor
Total labor per patient day
* Does not include labor for IV batch refill, returned medications and ADC stock-outs
** Includes ADC stock-outs
12
Since all processes impacting pharmacy workload were not measured in each of the seven hospitals, the study estimated
missing data for IV batch, ADC stock-out and ADC restocking of unloaded medications pathways by performing
calculations based on measurements gathered in the other hospitals. Table 11 provides the estimated labor adjustments
for the missing processes.
Table 11: Labor adjustments for missing processes
Figure 4 shows the relationship between the adjusted total pharmacy labor per day for a 300-bed hospital as a function
Hospital
1
2
3
4
6
7
Pharmacist
0:03:47
0:03:01
0:02:24
0:03:50
0:01:02
0:01:15
Technician
0:23:00
0:18:19
0:14:14
0:09:12
0:09:08
0:05:47
Current total labor per patient day
Workload estimates for missing processes
1. Estimated IV batch labor per
patient day
Pharmacist
0:02:29
0:00:49
0:00:07
Technician
0:06:37
0:02:10
0:00:19
2. Estimated labor for ADC
stock-outs per patient day
Pharmacist
0:00:00
0:00:02
0:00:01
0:00:03
0:00:03
Technician
0:00:03
0:00:32
0:00:15
0:00:50
0:00:59
3. Estimated labor to restock ADC
unloads per patient day
Technician
0:00:18
Total labor per patient day including missing processes
Pharmacist
0:03:47
0:03:02
0:04:54
0:03:50
0:01:53
0:01:26
Technician
0:23:03
0:18:51
0:21:07
0:09:12
0:12:08
0:07:04
Total labor per patient day
0:26:50
0:21:54
0:26:01
0:13:02
0:14:01
0:08:30
Total labor/day for a 300-bed hospital including missing processes
Pharmacist
18:54:31
15:11:54
24:30:52
19:09:56
9:26:36
7:08:59
Technician
115:16:37
94:15:39
105:33:34
45:59:16
60:40:27
36:21:38
Total labor/day for a 300-bed
hospital
134:11:08
109:27:33
130:04:26
65:09:12
70:07:03
42:30:37
A check ( ) in a cell means that the corresponding process for the corresponding hospital was missing and has been estimated.
13
of the percentage of total medications managed through the ADC pathway. This relationship suggests that pharmacy
labor for a 300-bed hospital decreases from a high of 134:11:08 for a 4% ADC medication hospital to 42:30:37 for a
95% ADC medication hospital at a rate of 4.1879 hours for each percentage point increase in the medication distributed
through ADCs. Converting these hours to full time equivalent (FTE) positions by computing the annual hours required
and dividing by 2,080 (FTE hours per year), the 4% ADC hospital would require 3.32 pharmacist FTEs and 20.23 technician
FTEs. By comparison, the 95% ADC hospital would need 1.25 pharmacist FTEs and 6.21 technician FTEs to accomplish
similar outputs.
The pharmacy labor difference is due to the elimination of both the NVA and additional process steps for
medications shifted from the unit dose and sterile processing pathways to the ADC pathway.
Figure 4: Adjusted pharmacy labor per day for a 300-bed hospital excluding order review/entry and
delivery of initial doses
Conclusion
144:00:00
25%
h:mm:ss
120:00:00
Workload
4%
Best fit
23%
96:00:00
80%
72:00:00
66%
48:00:00
y = - 4.1879 x + 5.8751
95%
24:00:00
R 2 = 0.9345
0:00:00
0%
20%
40%
60%
80%
100%
% Doses in ADC
14
For hospitals to reduce patient morbidity and mortality, it is imperative that appropriate medication therapy
be initiated rapidly and continued therapy be administered as scheduled. Pharmacy, nursing and technological
processes must all be synchronized to provide the correct medication, supplies and information for each one
of these transactions. In the seven hospitals analyzed with the medBPM methodology, as the percent of total
medication doses managed through ADCs increases, the data indicates:
• A rapid decrease in the time to initiate medication therapy
• Improved predictability related to when and where to look for medications to initiate therapy
• A reduction in missing doses and nurse non-value added activities related to missing doses
• A reduction in both pharmacist and pharmacy technician labor
Within the ADC pathway, decreasing the NVA steps and eliminating additional processing steps were the
major factors that contributed to the decrease in time to initial dose, the decrease in pharmacy labor and the
increased predictability of medication availability on the patient are areas. Getting medications started quickly
and adhering to prescribed therapy schedules is the core of medication safety.
References
1 Chan E., A. Ramudhin, An Evaluation of Various Business Process Modeling
Techniques Applied to Healthcare, ISEM 07, Beijing, May 2007
© 2010 CareFusion Corporation or one of its subsidiaries. All rights
reserved. medBPM is a registered trademark of BlueSail Solutions Inc.
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