Analysis of the medication management system in seven hospitals

Transcription

1 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

2 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

3 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 3 Med room Medication Travel 5 Triggered by 4 Pathway/KPIs 7 Location Metrics Information MedAdmin 00:02:00 $0.44 Med room 2 Find medication Mean Time = 00:02:00 Std. Dev. Time = 00:00:00 Cost=0.44 MAR 2 MedAdmin 00:03:00 $0.66 Med room Patient room 4 Go to patient room 200ft Mean Time = 00:01:00 Std. Dev. Time = 00:00:24 Cost=0.22 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 Medication 2 Medication Triggered by Scheduled Med 1 Provider Patient/Provider Interaction 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

4 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 % of doses in profiled ADCs 4% 23% 25% 66% 76% 80% 95% Average daily census Average number of daily medication orders 3,500 1,596 1,600 2,900 2,186 N/A 4,222 Technology 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 Patient care areas studied Medical surgical unit Critical care unit 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 () to measure how rapidly hospitals initiated therapy. is defined as the period of time starting when the medication was prescribed and concluding after the initial dose was administered and documented. 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

5 Our findings demonstrate that the most significant factor influencing 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. Figure 2 illustrates the relationship between the weighted average and the percent of medications dispensed through ADCs. Figure 2: Weighted average total time to initial dose () 1:40:48 1:26:24 1:12:00 0:57:36 0:43:12 0:28:48 0:14:24 0:00:00 Poly.() Y= x x x R 2 = % 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 4% 23% 25% 66% 76% 80% 95% for all medications 1:14:02 0:51:52 1:24:00 1:18:23 1:08:29 0:49:36 0:31:26 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 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

6 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 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 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 MS MS MS CC MS CC MS CC MS CC MS CC Oral 0:01:47 0:01:47 0:01:37 0:01:37 0:04:17 0:00:44 0:02:06 0:02:00 0:03:05 0:00:51 0:03:20 0:02:36 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 0:02:47 0:02:21 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 0:02:00 0:03:43 0:03:15 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 0:01:27 0:02:28 0:02:25 0:03:49 0:01:35 0:03:29 0:02:56 6

7 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 0:06:00 0:05:00 0:04:00 0:03:00 0:02:00 0:01:00 0:00:00 MS2 MS1 CC 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 MS1 MS2 MS1 CC MS1 CC MS1 CC MS1 CC MS1 CC Number of medication locations for scheduled doses Number of medication locations for initial doses Walking time from primary medication location to patient () Walking time from medication information to patient () :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 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

8 , 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 ADC pathway Unit dose pathway Sterile preparation pathway Wait queue for medication order entry Wait queue for medication preparation 1 1 Wait queue for medication verification 1 1 Wait queue for medication delivery 1 1 Total 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. 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

9 Table 7: Actual number of NVA steps by medication dispensing pathway Hospital % Doses in ADCs 4% 23% 25% 66% 76% 80% 95% NVA steps NVA steps NVA steps NVA steps NVA steps NVA steps NVA steps 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: :43: :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 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 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 (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 (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. 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 analysis: the higher the percentage of medications available in the ADC pathway, the lower the, 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

10 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 Percent of doses in ADC 4% 23% 25% 80% 95% Missing doses per patient per day Pharmacy estimate <.03 Nursing estimate n/a minimal minimal Nursing non-value added (NVA) steps per missing dose n/a n/a n/a Nursing additional time 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

11 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

12 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 Average number of daily orders 3,500 1,596 1,600 2, ,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 Number doses per patient day 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 Medications replenishment labor 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 Total labor 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 Total labor per patient day 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 * Does not include labor for IV batch refill, returned medications and ADC stock-outs ** Includes ADC stock-outs 12

13 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 Current total labor per patient day 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 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

14 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 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 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 144:00:00 120:00:00 4% 25% Workload Best fit 96:00:00 72:00:00 48:00:00 24:00:00 0:00:00 23% 66% y = x R 2 = % 95% 0% 20% 40% 60% 80% 100% % Doses in ADC 14

15 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 CareFusion Corporation or one of its subsidiaries. All rights reserved. medbpm is a registered trademark of BlueSail Solutions Inc. DI2097 (1110/3000) CareFusion San Diego, CA carefusion.com