just changed) the patient's medication from IV to PO. ... length oftreatment, morejudicious empiric therapy, and the use of ... real time when the provider orders medications. Tierney2 ... adverse events, and reduce the cost of care. The order.
An Information System to Promote Intravenous-to-Oral Medication Conversion Jonathan M. Teich M.D., Ph.D., Anna M. Petronzio M.A., Julia R. Gerner M.S., Diane L. Seger RPh, Caroline Shek RPh, John Fanikos RPh Clinical Systems Research & Development, Partners HealthCare System, Boston, MA Department of Pharmacy Services, Brigham and Women's Hospital, Boston, MA Many inpatients remain on expensive intravenous medications, even after they become able to take bioequivalent oral alternatives. We developed a computer intervention to identify such patients and to deliver alerts suggesting a switch to the oral medication. In thefirst phase of the project, alerts were delivered to pharmacists. The Brigham Integrated Computer System (BICS) was used to produce a daily report of patients receiving any of six targeted intravenous medications, who also had orders for an oral diet or other scheduled oral medications. Staff pharmacists screened the report and suggested IV to PO conversion in appropriate cases to the patient's nurses and/or physicians. Feedback was documented in the BICS system. Analysis of the pilot study showed that in 31.7% of cases, physicians agreed to change (or had just changed) the patient's medication from IV to PO. Further analysis ofpilot (Phase I) data was performed against a variety ofparameters in order to increase the ftaction of alerts deemed appropriate for conversion. These more specific alerts can be sent directly to
compromising medical care', by promoting appropriate length of treatment, more judicious empiric therapy, and the use of therapeutically equivalent, less expensive medication alternatives. Clinical information systems, particularly provider order entry, provide a potent method for promoting these changes, because they collect enough information to determine a patient's therapeutic needs, and can present recommendations in real time when the provider orders medications. Tierney2 demonstrated that overall charges can be substantially reduced with the use of inpatient order entry. Our group3 has documented several examples of interventions that have produced substantial savings on single drugs and drug families. A common example of excessive drug expenditures involves the use of expensive intravenous (IV) medications. Frequently, a patient is admitted to the hospital in serious condition, and IV medications are prescribed. The patient may later improve and become able to tolerate oral (PO) administration, which for many drugs produces comparable drug levels at a substantially lower cost. Despite the improvement in the patient's condition, they will often remain on the IV. Misperceptions that an intravenous medication is more effective than its oral equivalent often keep the patient
physicians.
INTRODUCTION Drug therapy accounts for a significant fraction of inpatient costs. Past analysis has suggested that these costs could be reduced significantly without Medication IV- cost/ dose Metronidazole 500 mg $1.69 / bag
PO -cost / dose $0.04/ tablet
Cost savings per day $4.95 (500 mg q8h)
Fluconazole 100 mg Fluconazole 200 mg Fluconazole 400 mg
$32.81/ bag $65.62/ bag $95.91/ bag
$5.52/ tablet $9.09/ tablet $18.18/ 200 mg x2
$27.28 (100 mg q24h) $56.53 (200 mg q24h) $77.33 (400 mg q24h)
Ofloxacin 200 mg Ofloxacin 400 mg
$4.42 / bag $8.85 / bag
$2.58 / tab $3.24 / tab
Ketorolac 30 mg
$3.33 / syringe
2879
162,750
$3.68 (200 mg bid) $11.22 (400 mg bid)
11280
126,562
4032
52,900
23214
74,981
9265
387,184
Oxycodone/APAP $0.04
$13.12 (30 mg iv q6h)
Ibuprofen 600mg $ 0.05
(1 tablet po q6h)
$3.23 (50mgiv q8h) (150 mg po bid) $41.79 (8mg q8h)
Ranitidine 50mg
$1.15 / bag
Nizatidine 150mg $0.11
Ondansetron 8 mg
$28.54/ bag
$14.61/ tablet
pt-days/yr Saved/yr 11670 $57,766
Figure 1. Potential daily and annual savings from IV-to-PO conversion. # pt-days/year only includes patients taking PO nutrition or drugs. 1091-8280/99/$5.00 © 1999 AMIA. Inc.
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on this track. Guidance from a pharmacist, or from an information system, may help promote IV-to-PO
conversion.' At Brigham & Women's Hospital (BWH), special emphasis was placed on six medications (Fig. 1). Theoretically, if all use of these medications was in the oral form whenever the patient demonstrated an ability to take oral medication (i.e., if the patient was taking oral nutrition or other oral medications), an annual saving of over $850,000 would result. Although some use of the IV form is appropriate, even a fraction of this saving would be significant. In addition, the use of oral medication may allow a reduction in length of stay. One study5 of antibiotic IV-to-PO conversion demonstrated $15,000 drug cost saving and a $160,000 saving due to reduced length of stay.
the large majority of clinical decision support in BICS is directed to physicians, we were concerned that our initial empiric rules determining which IV orders were eligible for PO conversion were too non-specific. Our experience suggested that physicians, faced with a class of reminders that were often inappropriate, might ignore all interventions in the class. The Pharmacy Services department was particularly motivated to decrease drug therapy costs; they were willing to accept the intervention at a relatively non-specific level, allowing a test of the intervention logic before proceeding to direct physician contact. In Phase I, BICS was used to select patients taking one of the six targeted IV medications, who were also (a)ordered for an oral diet, or (b)ordered for other scheduled (non-PRN) oral medications. At midnight each day, the computer compiled an IV-PO Report, listing patients that matched the selection criteria. The report displayed, for each patient, the patient's name, medical record number, hospital room, diet, current IV
We developed an intervention strategy to promote conversion of IV medication orders to equivalent PO alternatives, in an effort to decrease drug therapy costs. This paper reports on the study design, implementation, results, and lessons learned. METHODS
Brigham and Women's Hospital (BWH) is a 700-bed academic medical center in Boston.. The Brigham Integrated Computing System (BICS)3 provides clinical, administrative, and financial computer services to the hospital. The system runs on a local area network of 7000 personal computers; workstations are located in all clinical areas. Most BICS applications were developed in-house in Mumps. A computerized provider order entry system6 captures all inpatient physician orders. The system provides a wide variety of alerts, reminders, and other clinical decision support interventions designed to promote optimal care strategies, reduce adverse events, and reduce the cost of care. The order entry system provides the foundation for the IV-to-PO intervention strategy.
medication orders, and current (Figure 2). Figure 2. IV-to-PO conversion report. Each morning, a staff pharmacist printed the IV PO Report for his or her assigned inpatient units. The pharmacist brought the report to the unit, and reviewed each patient's clinical status to see if the patient was indeed able to take oral medications. If the pharmacist was uncertain about the patient's ability to take PO medications, he or she spoke with the patient's nurse. The pharmacist would then make a decision on whether the patient could be switched from IV to PO. If the patient was able to change, the pharmacist contacted the physician and suggested changing the medication order. In practice, this contact usually took place via email. The pharmacist recorded feedback information electronically in BICS, using a form that captured the clinician's response to the alert.
For this intervention project, six intravenous medications were selected due to the their excellent oral bioavailability, potential for cost savings, and frequency of possibly unnecessary use. The targeted medications were fluconazole (Diflucan), ketorolac (Toradol), ranitidine ondansetron (Zofran), (Zantac), metronidazole (Flagyl) and ofloxacin (Floxin).
The project has been designed in a number of experimental phases. In Phase I, computerized alerts were directed to pharmacists as a daily report. Although
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RESULTS Phase I was performed over a three month period. The primary measure was the clinician response to the alert. Analysis showed that 31.7% of the physicians had changed or said they would change the patient's medication from IV to PO. An additional 5% of responses officially classified as "Other" actually represent positive responses as well. In particular, in 16.4% the physician directly followed the pharmacist's request to change the order, while in 15.3% of the cases the physician stated that the medication had already been discontinued or changed between the time the report was created and the time of the pharmacist's review. In this latter case, it appears credible that the recommendation was correct, although in terms of overall compliance these are neither positive nor negative responses.
However, it did point out the imperative of improving the prediction rule so that alerts could bypass the pharmacy middleman and be delivered directly to the physicians, who has prior knowledge of the patient's status.
This positive response rate is in itself large enough to produce moderate savings, if it could be repeated at scale. As with any intervention, however, it is worthwhile to understand the reasons for rejection; this can lead to a better intervention (if there are legitimate reasons for rejection), or to a drive for more education (if the reasons for rejection are not appropriate). In 44.8% of cases, the response indicated that the clinician felt the patient was not ready to take PO medications, even though the order record showed that the patient was ordered for other medications or food orally. For example, some patients were recently post-op and were not taking anything by mouth, even though an oral diet order was still active. This order was technically erroneous, but the inpatient nursing staff implicitly understood that the order was to be ignored. Other frequent reasons for rejecting the recommendation were patients with mucositis (who received oral medications only for treatment of that condition), patients in the ICU, and patients who were still on their first full day of the IV medication, where the physician felt that it should be continued for a presumed therapeutic advantage.
PROCESS IMPROVEMENT After Phase I, we set out to improve the project in several ways: improving the prediction rule, managing the full course of a drug order, capturing reasons for acceptance and rejection more precisely, and preparing to send the alert directly to clinicians. The prediction rule sensitivity analysis is of particular interest.
Logistics. The pharmacist-based intervention coincided with an acute shortage of pharmacy manpower at the hospital. The reduced staff had less time to examine the IV-PO report, review charts, and contact nurses and physicians. The large number of alerts generated under the Phase I rules (approximately 130/day) were too many for the staff to handle. No processing at all was done on weekends. When time did permit the pharmacist to process the IV-PO report, their analysis was performed carefully; there was no reason to suggest that the manpower shortage invalidated the above findings concerning the percent of acceptable alerts.
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The Phase I results also suggested new data items that should be captured to better understand whether the intervention would actually reduce medication costs. It is important to capture length of IV drug use after an order first makes the report; whether a repeat alert would be effective on a subsequent day after an initial rejection; and whether the drug was actually discontinued or changed after the physician agreed to do so. BICS routinely captures some of these data elements; others need to be captured when an alert is reviewed.
As mentioned above, certain conditions correlated with refusal to change to a PO alternative. To investigate this further, we did statistical analysis of the Phase I data against several parameters including:
specific diet type (NPO, tube feeding or oral); diet consistency (clear liquid, regular, soft, etc.); which specific classes of oral medications the patient was receiving; * the patient's other intravenous medications; * whether the patient was in an ICU; * the post-operative day, if applicable. These were chosen because they were among the most common reasons for rejection stated in Phase I. In addition, we could form hypotheses explaining why each of these factors could affect the IV-to-PO decision. Using clinician agreement with the alert as the gold standard, parameters were varied so as to make a new rule for each medication, and the new rule was optimized by running it retrospectively against the universe of all Phase I alerts. The optimal rule was selected to balance high sensitivity (defined as the percentage of phase I accepted alerts that would still have been generated under the new rule) and high positive predictive value (PPV, defined as the percentage of all alerts generated, under the rule, that would have met with clinician agreement). Table I * * *
shows the values of these parameters achieved with the new rules. Ofloxacin was removed from the intervention due to changes in drug policy and drug usage.
Medication
New
Old
DISCUSSION In the early stages of order entry on BICS, decision support interventions were simple: allergy checking, duplicate therapy alerts, simple drug substitutions. As order entry became a routine part of the daily life at BWH, and as its value in preventing adverse events and reducing costs was demonstrated', more complex interventions were implemented, including structured ordering with feedback', and adverse-drug-event monitoring9. IV-to-PO conversion is an advanced intervention, requiring a broad array of data and complex logic for optimal sensitivity and specificity; however, the potential benefits in medication costs and earlier discharges make it well worth implementing. Automation of the process helps select only those patients who are likely candidates for conversion, thus minimizing pharmacist time and energy9. The initial alerting criteria (patient on an oral diet or other oral medications and on one of the targeted IV medications) proved to be too nonspecific and led to a large volume of rejected alerts. The new optimized rules reduce the end-user workload.
New
____________IPPV, % PPV, % Sensitivity Metronidazole Fluconazole
72 83
28 35
54 83
Ketorolac Ranitidine Ondansetron
93 68 62
65 43 20
75 61 52
Table 1. PPV and sensitivity with new predictive rules. By definition, the "sensitivity" of the old rule is 100% (the universe is the set of alerts under the old rule).
The new predictive rules are complex, and different for each of the remaining five medications. For example, in order to alert for a possible ondansetron conversion, the patient must be on an oral diet and not in the ICU (ICU patients are almost totally excluded by the new rules). In addition, one of the following must be true:
While it is clear that IV-to-PO conversion reduces the overall cost of care, it has been argued that the savings to the hospital itself vary with the patient's insurance plan. With a fully capitated patient, increasing costs may be passed directly to the hospital; with a fee-for-service patient, the actual impact on the hospital's finances could be minimal, or even positive. Such a line of argument, followed with only the hospital's bottom line in mind, could lead to interventions that differ solely based on the payor. While this already happens routinely with payor-specific drug formularies for essentially equivalent drugs, it may be more problematic (both technically and ethically) to stratify patients in this fashion for payor-based care guidance.
(a) The patient is not on any other IV medications and they have been on ondansetron IV for less than 5 days;
OR, (b) The patient is on at least one other PO medication, fewer than five other IV medications, has not had surgery, and is not on any antacids. While the new rules appear to be more efficient, they must be validated against a distinct data set.
The new predictive rules have resulted in a decrease in the volume of patients on the IV PO Report. Where previously 130 patient alerts were flagged on any given day, 43 alerts are now being flagged.
The new rule set is complex, and is based on a statistical analysis rather than a simple principle. It is unlikely that physicians consider such a complex rule when making their own unaided decisions to convert IV medications. Our phase I results show that it is difficult to empirically state any simple rule at all that physicians uniformly follow in this regard. Their reasons for accepting or rejecting the alert may be capricious, or may be implicitly and intuitively based on rules such as the ones we derived statistically. Our predictive rules may be further modified after data collection from large-scale alerting is completed.
In Phase II, BICS sends alerts as a report to the pharmacist again; the pharmacy operation of the project has been adjusted to better handle the alert volume. Interviews were conducted with BWH Pharmacy staff to determine how the IV-PO Report could be improved. The new report includes more information to streamline the process for the pharmacists, such as covering physician name and pager number, as well as the start dates for both the diet and intravenous medication. The feedback function was also revised in order to improve on the data being collected, as well as to help streamline the feedback process for Pharmacy staff.
We have expressed concern that physicians would not accept a class of automated reminders if there were too many false-negative alerts. This concern is based on
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information systems, backed by appropriate data sets including order entry, show great potential for delivering both simple and complex clinical decision support.
subjective factors, including negative response to interventions that are perceived as producing more work without conveying more information'. BICS provides a large number of clinical decision support interventions, and these have in general been very well accepted by ordering physicians. We believe that it is important to protect this acceptance by ensuring that a given alert is highly likely to be relevant. The pharmacy department one of the main stakeholders of the intervention - was motivated to take on the effort of working with the intervention in its early phase until we could fine-tune its predictive value. This is an important consideration for pilot studies of interventions.
REFERENCES
1 Mutnick AH, Sterba KJ, Peroutka JA, et. al. Cost savings and avoidance from clinical interventions. Am. J. Health Syst. Pharm 1997; 54(4):392-6. 2 Tierney WM, Miller ME, Overhage JM, et al. Physician inpatient order entry writing on microcomputer workstations. JAMA 1993; 269:379383. 3 Teich JM, Glaser JP, Beckley, RF, et al. Toward costeffective, quality care: The Brigham integrated computing system. Proc Ann Nicholas E. Davies CPR Recognition Symposium 1996, 2: 3-34. 4 Zamin MT, Pitre MM, Contry JM. Development of an intravenous-to-oral route conversion program for antimicrobial therapy at a Canadian tertiary care health facility. Ann. Pharmacother. 1997; 31(5):56470. 5 Przybylski KG, Rybak MJ, Martin PR, et. al. A pharmacist-initiated program of intravenous to oral antibiotic conversion. Pharmacotherapy 1997; 17(2):271-6. 6 Teich JM, Hurley JF, Beckley RF, Aranow M. Design of an easy-to-use physician order entry system with support for nursing and ancillary departments. Proc SCAMC 1992;16:109-113. 7 Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998, 280(15):1311-1316. 8 Harpole LH, Khorasani R, Fiskio J, Kuperman GJ, Bates DW. Automated evidence-based critiquing of orders for abdominal radiographs: impact on utilization and appropriateness. J Am Med Inform Assoc. 1997 Nov; 4(6): 511-521. 9 Jha AK, Kuperman GJ, Teich, JM, et al. Identifying adverse drug events: development of a computerbased monitor and comparison with chart review and simulated voluntary report. J Am Med Inform Assoc 1998, 5(3):35-314. 10 Lee FY, Teich JM, Spurr CD, Bates DW. Implementation of physician order entry: user satisfaction and self-reported usage patterns. J Am Med Inform Assoc 1996; 3:42-55.
BICS interventions have acceptance rates that vary from 30% to 96%3. Most interventions are in the upper half of this range, representing relatively non-controversial therapy substitutions or reminders about forgotten hazardous conditions. When interventions have low acceptance rates, it is important to determine whether it is because the intervention itself is clinically inappropriate or nonspecific, or whether more education is needed to convince the ordering physicians of its value. We provide feedback on compliance to the clinical proponents of the intervention, so that they can work with the ordering physicians directly to investigate any difference of opinion. Measurement of the impact of this intervention could be affected if physicians learn to expect the alert. We can continue to study the specific response rate to an alert, and we can also look for a change in overall patientdays on the IV or PO form of a drug. While the first may be more specific to the intervention, the second accounts for the fact that learning may take place as alerts occur. Physicians who have received alerts may become more conscious of the cost of these medications, and may be inclined, in the future, to order the PO form in the first place.
FUTURE PLANS We are now implementing alerts directly on the physician's daily order renewal screen, in the third phase of this project. Bringing the intervention to the physicians after refining the process should increase the number of alerts that can be appropriately reviewed each day. Measurement in Phase II and Phase III is designed to more completely understand the clinician response to an alert, to accurately test the prediction rules, to study whether alerting physicians directly is more effective than alerting pharmacists by a report, and to measure actual impact of the intervention on Clinical medication costs and length of stay.
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