In our March 2011 What’s New in the Patient Safety World column “Downside of Transfusions in Surgery” we discussed the mounting evidence that transfusions during surgery are associated with increased morbidity and mortality. We also noted that some performance improvement programs were successful in reducing the frequency of transfusions and resulted in considerable cost savings.
A new study in children looked at the impact of clinical decision support tools on transfusions (Adams 2011). The investigators developed evidence-based rules to alert physicians if parameters were outside those recommended for transfusion when a physician ordered RBC transfusions. The rate of RBC transfusions dropped significantly both on the pediatric wards and the PICU after implementation of the CPOE rule compared to historical controls. The rule implemented was fairly simple: when an order for an RBC transfusion was placed, the system checked BP stability over the previous 6 hours and the most recent serum hemoglobin level (within the last 24 hours). If the patient had been normotensive for at least 6 hours and the Hgb was greater than 7, an alert popped up that notes the evidence against transfusion in this scenario. The clinician could still proceed with the order (it was not a hard stop, nor did it require an explanation for overriding the alert). The alert was associated with over a 50% decrease in transfusions on the acute care wards and a lesser decrease in the PICU. Estimated direct savings (on blood costs alone) were greater than $165,000 for this hospital. Indirect savings (eg. from avoiding the unwanted consequences of transfusions) undoubtedly raise the net savings.
Not all attempts to use clinical decision supports within CPOE have been successful in reducing unnecessary transfusions. At Brigham and Women’s Hospital (Scheurer 2010) studied appropriateness of transfusions 2 years after transfusion guidelines were instituted and clinical decision support tools implemented within CPOE. Over half the transfusions ordered were still considered inappropriate 2 years after implementation. It was found that decision support was bypassed altogether in two-thirds of transfusion orders (by indicating “active bleeding” even though chart review failed to substantiate that in almost half the cases) and that over two-thirds of the overrides indicated a superior had instructed the transfusion. The authors conclude that clinical decision support, by itself, is not likely to eliminate inappropriate transfusions and that other front-end interventions aimed at the decision maker are likely needed. The authors felt that this study showed that the decision to transfuse had “already been made” prior to the CPOE so that, in effect, the clinical decision support was rendered too late. In addition, they felt that CPOE targeted the intern or more junior resident in most cases and might be better directed toward the more senior clinicians making the decision to transfuse.
It is not clear why differences were seen in the two populations. In the pediatric study it was not mentioned who was doing the order entry (though this was also an academic hospital). They also specifically excluded some units (eg. hematology/oncology, cardiology, and NICU) and there was a difference in the case mix index between the historical control and study period.
Obviously there are multiple factors in play that are important in the potential success of clinical decision support tools to impact the appropriateness of transfusion.
Adams ES, Longhurst CA, Pageler N. Computerized Physician Order Entry With Decision Support Decreases Blood Transfusions in Children. Pediatrics 2011; 127(5): e1112 -e1119 (doi: 10.1542/peds.2010-3252)
Scheurer DB, Roy CL, McGurk S, Kachalia A. Effectiveness of Computerized Physician Order Entry with Decision Support to Reduce Inappropriate Blood Transfusions. JCOM 2010; 17(1): 17-26
The New England Journal recently has introduced a number of educational video tools on a variety of topics in clinical medicine. One of the most recent is one on “Conscious Sedation for Minor Procedures in Adults”. It’s organized into 13 “chapters”, running the gamut from the credentialing, pre-procedure evaluation, preparation, equipment, monitoring, medications, potential complications, and recovery. It lasts about 13 minutes in all. The video has a very good on discussion of commonly used medications, their half-lifes, and potential need for more than one pharmacological reversal. It also highlights the need for at least two clinicians being involved (one to do the procedure, the other to monitor the patient). It also provides good advice on what clinical features of the patient should merit having an anesthesiologist administer the conscious sedation.
While this video is not enough, by itself, to use as your training material for clinicians requesting facility privileges in conscious sedation, it is a very good adjunct that could be added to your other materials.
The New England Journal now has an excellent collection of video tools, particularly for training on a variety of procedures commonly used in clinical medicine.
Jones DR, Salgo P, Meltzer J. Conscious Sedation for Minor Procedures in Adults.
N Engl J Med 2011; 364: e54 June 23, 2011
New England Journal of Medicine. Videos in Clinical Medicine.
We often lament that our supermarkets better use technology than our hospitals do. Our supermarket can tell us exactly how many boxes of cornflakes there are and exactly where they are in the store. Most of the hospitals we work with cannot tell us how many orthopedic widgets they have and where they are.
Online retailers like Amazon.com use populational databases to predict what someone might want to buy. How often have you bought something on an online site and saw a message that says something like “People who have purchased “X” have also often purchased “Y”? Great marketing tool!
But now healthcare researchers have taken that concept in attempt to improve the medication reconciliation process. Initial lists for medication reconciliation (sometimes called “best possible medication history” or similar names) very frequently omit important drugs that a patient has actually been taking. Hasan and colleagues (Hasan 2011) have used the above concept, which they refer to as collaborative filtering, to help identify medications omitted from patient medication lists at the time of medication reconciliation. They basically determine, based on large population databases, that patients who take drug “X” also often take drug “Y”. They established multiple different algorithms and applied them to sample patient data. In fact, their algorithms were able to guess correctly an omitted drug within 10 guesses about 50% of the time (they did even better guessing the therapeutic class of a missing drug). They found some of their algorithms might work better in certain settings or with certain populations.
While this is not yet ready-for-prime-time, and might be expected to produce some unintended consequences, it is a fascinating concept that we expect someday will prove to be very useful in improving the medication reconciliation process. Think about how this could also be used to improve another patient safety problem – diagnostic error.
Keep your eyes on this technology!
Hasan S, Duncan GT, Neill DB, Padman R. Automatic detection of omissions in medication lists. JAMIA 2011; 18: 449-458 Published Online First: 29 March 2011
Our June 2010 What’s New in the Patient Safety World column “The July Effect: Myth or Reality?” discussed the “July effect” in which less than optimal care is suspected to occur in academic hospitals when housestaff turn over. For years many have warned that the most dangerous time to be admitted to hospitals in the US was in July when new housestaff come on board. However, numerous studies in the past have been unable to corroborate that with good evidence.
A new systematic review (Young 2011) concludes that the “July effect” does exist. The authors reviewed 39 studies, spanning several decades, and noted that the studies are very heterogeneous and have conflicting conclusions. However, the studies of higher quality and larger sample sizes did show that mortality rates are higher and measures of efficiency lower during this period of year-end changeovers. They also note that there is almost no data regarding outcomes in ambulatory settings.
But the contributing factors are less clearcut, as pointed out by Young and colleagues and the accompanying editorial (Barach 2011). While the most obvious factor is new, relatively inexperienced housestaff coming on board, most studies lack details about the levels of supervision. But Young et al. are quick to point out that it is not just medical knowledge inexperience but also lack of familiarity with the new surroundings that may be important. You have to learn to navigate your way through the new system. In that regard, it might be useful to compare outcomes between those systems where residents are in only one hospital vs. those multi-hospital systems where residents rotate through a different hospital each month.
In our prior column we noted a study (Phillips and Barker 2010) that demonstrated a consistent July spike in deaths inside medical institutions coded as being due to fatal medication errors but only within counties having teaching hospitals. Moreover, the July spike was greater in those counties having a greater concentration of teaching hospitals. They found no similar spike for deaths of all causes or for deaths due to adverse medication effects (i.e. those medication-related deaths felt not to be preventable). Though the authors did consider potential alternative explanations (eg. more vacations in July, summer spikes in alcohol use and trauma, etc.) they conclude the most likely explanation for the “July effect” is the influx of new housestaff in teaching institutions. They also did not find evidence of a change in the July spike as new residency work hour rules came into effect.
Remember, everyone is changing over in July. You now have physicians who were interns yesterday becoming the supervisors of new interns today. And most teaching hospitals also have an influx of new nurses and maybe pharmacists around the same time. Some teaching hospitals also have new attendings starting and they often draw the summer months for their service commitments. The impact of vacations for healthcare personnel at all levels is another factor (remember that even vacations by clerical staff may put an additional burden on clinical staff). And summer vacation for school-agers also leads to more trauma and injuries related to outdoor activities and alcohol.
And before you blame the new first year housestaff, keep in mind our December 8, 2009 Patient Safety Tip of the Week “” which mentioned the (Dornan 2009) done in the UK on prescribing errors. Though originally established to look at prescribing errors made by first year residents, that study demonstrated that prescribing errors were both common and made by physicians at all levels. Looking at over 100,000 medication orders across 20 hospital sites, they found an average error rate of 8.9 errors per 100 medication orders. The error rate for first year residents, responsible for about half the orders, was 8.4% - actually lower than that for the entire group. All physician levels, including attendings, made prescribing errors. The highest rate (10.3%) was actually seen for second year residents. So perhaps some of the “July effect” might be due to staff at more advanced levels.
Young et al. do have some suggestions for possible interventions, including using more experienced (“our best attendings”) physicians as supervisors during July, minimizing housestaff fatigue, reducing the trainee workloads, making better use of physician extenders and better use of multidisciplinary teams, and even possibly staggering scheduled starts for new trainees.
Clearly, more research needs to be done to identify all the factors that may play a role in the “July effect” and more scientifically evaluate various interventions aimed at avoiding it.
Young JQ, Ranji SR, Wachter RM, et al. “July Effect”: Impact of the Academic Year-End Changeover on Patient Outcomes. A Systematic Review. Ann Intern Med 2011; 154: 000-000 published ahead of print July 11, 2011
Barach P, Philibert I. Editorial: The July Effect: Fertile Ground for Systems Improvement. Ann Intern Med July 11, 2011 E-352; published ahead of print July 11, 2011
Phillips DP, Barker GEC. A July Spike in Fatal Medication Errors: A Possible Effect of New Medical Residents. J Gen Intern Med 2010; published online first June 2010
Some of our prior columns related to wrong-site surgery:
Patient Safety Tip of the Week columns:
September 23, 2008 “”
June 5, 2007 “ ”
March 11, 2008 “Lessons from Ophthalmology”
September 14, 2010 “ ”
November 25, 2008 “Wrong-Site Neurosurgery”
January 19, 2010 “Timeouts and Safe Surgery”
June 8, 2010 “Surgical Safety Checklist for Cataract Surgery”
December 6, 2010 “ ”
June 6, 2011 “Timeouts Outside the OR”
What’s New in the Patient Safety World columns:
July 2007 “ ”
“Medication Issues in the Ambulatory Setting”). They do also mention self-medication as a possibility, though our own bias is that introduces too many risks in this setting. Ideally, in the age of CPOE and EMR’s, rules-based alerts could alert the physician or pharmacist or nurse about critical issues in this patient population. Unfortunately, clinical decision support systems currently in use in most hospitals have difficulty using diagnoses (particularly secondary ones as would be the case here) to trigger actions. Theoretically, one could probably identify most Parkinson’s patients by their medication profile and perhaps use that to trigger alerts. Maybe anti-Parkinsonian medications need to be added to your list of high-alert drugs!
The peripherally related article that jogged our memory about the above paper was one on medications prescribed for psychosis in Parkinson’s patients on an outpatient basis (Weintraub 2011). That study showed a large number of patients are treated with antipsychotic drugs that either may worsen the motor findings, have unproven or limited efficacy in patients with Parkinson’s, or are prescribed despite black box warnings about their use in patients with dementia. It is an interesting article about how some drugs become widely prescribed in certain populations despite evidence bases that suggest limited efficacy.
Parkinson’s is a tough disease for patients and their families/caregivers. The complexities and fragmented nature of our healthcare system further exacerbate the difficulties they have.