While there is a wealth of literature dealing with the association between workload and patient outcomes and costs for nurses and housestaff, there has been relatively little literature on the impact of attending physician workload.
A new study has looked at the impact of hospitalist workload on quality and efficiency of care (Elliott 2014). They found clinically meaningful increases in length of stay and costs associated with increased hospitalist workloads. They found that LOS and cost increased exponentially above a hospitalist census value of about 15 patients per day. Length of stay accounted for most of the excess cost but even after adjustment for length of stay cost increased by $205 per unit increase in hospitalist census.
Hospital occupancy was also a factor. For occupancies less than 75%, LOS increased linearly from 5.5 to 7.5 days from low to high workloads. At occupancies between 75-85% LOS was stable across lower workloads but increased to 8.0 days at higher workloads.
Importantly, they did not find associations between hospitalist workload and in-hospital mortality, rapid response team activation, 30-day readmissions, or patient satisfaction.
While the Elliott study found no adverse effects on patient safety issues related to higher hospitalist workloads, in a previous survey of hospitalists (Michtalik 2013) respondents strongly felt that excessive workloads did impact patient safety and outcomes. In that survey 40% of hospitalists responding felt that their typical inpatient workload exceeded “safe” levels at least monthly. The hospitalists felt that excess workload frequently interfered with full discussion of treatment options, led to delays in discharges (or admissions), more testing and consults, worsened patient satisfaction, and probably also contributed to patient transfers, morbidity and mortality. Keep in mind, however, that this study had no objective measurements of quality and patient safety and was based on the perceptions of the responding hospitalists.
In an editorial accompanying the Elliott study, Bob Wachter (Wachter 2014) notes that there are considerations other than simply patient census that need be taken into account. For example, hospitalists in most institutions play key roles in some of the quality improvement and patient safety initiatives. They may also have other administrative and/or teaching responsibilities. He, therefore, notes that the number 15 patients per hospitalist may not apply at all settings and in all circumstances.
The key is probably consideration of models to better intervene when physician workloads approach such thresholds.
Elliott DJ, Young RS, Brice J, et al. Effect of HospitalistWorkload on the Quality and Efficiency of Care. JAMA Intern Med 2014; Published online March 31, 2014
Wachter RM. HospitalistWorkload: The Search for the Magic Number. JAMA Intern Med 2014; Published online March 31, 2014
Michtalik HJ, Yeh H-C, Pronovost PJ, Brotman DJ. Impact of Attending Physician Workload on Patient Care: A Survey of Hospitalists. JAMA Intern Med 2013; 173(5): 375-377
Our What’s New in the Patient Safety World columns for September 2012 “FDA Warning on Codeine Use in Children Following Tonsillectomy” and March 2013 “Further Warning on Codeine in Children Following Tonsillectomy” described cases of death and serious adverse effects in children treated with codeine following adenotonsillectomy for obstructive sleep apnea. Those cases led to the FDA issuing a safety alert (FDA 2012) and additional cases led to a subsequent black box warning for products containing codeine (FDA 2013).
The original FDA alert was issued after reviewing reports in the literature of 3 deaths and one near-miss case of respiratory depression in young children (ages 2-5) following tonsillectomy and/or adenoidectomy for obstructive sleep apnea (Ciszkowski 2009, Kelly 2012). The most interesting facet is the data presented on unusual metabolism of codeine as a root cause. Ingested codeine is converted into morphine in the liver by cytochrome P450 2D6 (CYP2D6). It turns out there are genetic variations that cause some people to be “ultra-rapid metabolizers” which leads to higher concentrations of morphine earlier. Apparently all the children in the above reports were “ultra-rapid metabolizers”. The original FDA alert (FDA 2012) estimates the number of “ultra-rapid metabolizers” as generally 1 to 7 per 100 people, but may be as high as 28 per 100 people in some ethnic groups (the FDA site has a table of these rates by ethnic group).
In addition to those rapid metabolizers who are at risk for toxicity from codeine, up to a third of children are poor metabolizers of codeine and subsequently get little or no benefit of codeine for pain or other conditions such as cough (Kaiser 2014). For many years the American Academy of Pediatrics has recommended against use of codeine in children for either analgesia or cough. The American College of Chest Physicians has recommended against the use of codeine for cough in children since 2006. And the World Health Organization and health ministries in Canada and Europe likewise have recommended against use of codeine in children.
So one would anticipate that use of codeine in children would have almost stopped completely. Not so. A recent study (Kaiser 2014) has demonstrated that codeine continues to be prescribed to children in significant numbers. They analyzed emergency department visits for children between the ages of 3 and 17 from 2001 to 2010. Though the percentage of visits resulting in codeine prescriptions did drop from 3.7% to 2.9% over the study period, there was no decline in prescription rates after the 2006 guidelines recommending against use of codeine for cough or URI. For subgroups, the rate of codeine prescriptions did decrease significantly in the 3-7 year age group but not others. Overall, codeine continued to be prescribed to 500,000 to almost 900,000 children per year.
The authors note some potential interventions that might decrease the prescription of codeine in children including:
It’s pretty clear that guidelines and provider education have been inadequate in stopping use of codeine in children. If you do educational interventions, remember that stories are better than statistics. Be sure to include descriptions of cases in the original literature of 3 deaths and one near-miss case of respiratory depression (Ciszkowski 2009, Kelly 2012). But remember that education and training are what we consider to be weak actions. In our March 27, 2012 Patient Safety Tip of the Week “Action Plan Strength in RCA’s” we included some slides to help you remember which actions are strong and which are weak. Forcing functions and constraints that make it difficult to order or prescribe codeine for children are much more likely to be successful.
FDA. FDA Drug Safety Communication: Codeine use in certain children after tonsillectomy and/or adenoidectomy may lead to rare, but life-threatening adverse events or death. 8/15/12
FDA. FDA Drug Safety Communication: Safety review update of codeine use in children; new Boxed Warning and Contraindication on use after tonsillectomy and/or adenoidectomy. Update February 20, 2013
Ciszkowski C, Madadi P, Phillips MS, Lauwers AE, Koren G. Codeine, ultrarapid-metabolism genotype, and postoperative death. N Engl J Med 2009; 361(8): 827-828
Kelly LE, Rieder M, van den Anker J, Malkin B, Ross C, Neely MN, et al. More codeine fatalities after tonsillectomy in North American children. Pediatrics 2012; 129:5 e1343-e1347; published ahead of print April 9, 2012
Kaiser SV, Asteria-Penaloza R, Vittinghoff E, et al. National Patterns of Codeine Prescriptions for Children in the Emergency Department. Pediatrics 2014; 133(5): e1139-e1147 Published online April 21, 2014
In the past 3 years we’ve done multiple columns (see list at the end of today’s column) highlighting some of the detrimental effects related to red blood cell transfusions and the trend toward more restrictive transfusion strategies in many different scenarios. Unnecessary transfusions have not only clinical untoward effects but add to health care costs.
One of the untoward side effects of transfusion is the risk of infection. A new systematic review and meta-analysis on the relative risk of infection in restrictive vs. liberal transfusion strategies was just published (Rohde 2014). It showed the risk of serious infections was 11.8% with a restricted transfusion strategy vs. 16.9% with a liberal strategy. The number needed to treat (NNT) with the restrictive strategy to prevent one infection was 38. For every 1000 patients in which transfusion is under consideration 26 serious infections could be avoided by using the restrictive transfusion strategy.
The accompanying editorial by Jeffrey Carson (Carson 2014), author of several studies on the detrimental aspects of transfusion and lead author of the revised AABB guidelines on transfusion (see our April 2012 What’s New in the Patient Safety World column “New Transfusion Guidelines from the AABB”), highlights some of the other outcomes that benefit from a restricted transfusion strategy. He further highlights, however, that we still don’t know the optimal transfusion threshold/trigger, noting some evidence to suggest that may be even lower than the current guidelines (Carson 2012) suggest.
Have your organization’s transfusion policies and practices kept up-to-date with current trends and recommendations?
Prior columns on potential detrimental effects related to red blood cell transfusions:
Rohde JM, Dimcheff DE, Blumberg N. et al. Health Care–Associated Infection After Red Blood Cell Transfusion. A Systematic Review and Meta-analysis. JAMA 2014; 311(13): 1317-1326
Carson JL. Blood Transfusion and Risk of InfectionNew Convincing Evidence (editorial). JAMA 2014; 311(13): 1293-1294
Carson JL, Grossman BJ, Kleinman S, et al. for the Clinical Transfusion Medicine Committee of the AABB. Clinical Guidelines.Red Blood Cell Transfusion: A Clinical Practice Guideline From the AABB. Ann Intern Med 2012; 157(1): 49-58
A new easy-to-use scoring system for severity of delirium has been developed and validated (Inouye 2014). The tool, CAM-S, is based on the Confusion Assessment Method (CAM) which is already widely used as a screening tool for delirium. There are actually 2 forms, one a 4-item tool and the other a 10-item tool. Both were validated in 2 independent populations and have good inter-rater reliability. Increasing severity on these two scores is associated with worse outcomes (length of stay, hospital costs, nursing home placement, cognitive decline, death within 90 days, and functional decline at 30 days). Previous systems for scoring severity of delirium have not been widely adopted. The new system will likely be better utilized because of its relationship to the CAM and it doesn’t require special training to administer. The short form takes only 5 minutes to complete. The tool(s) may help follow the clinical progress of patients with delirium, determine whether the patient is improving and responding to management interventions, and be of prognostic value. They likely will also play a key role in future research and clinical trials.
The accompanying editorial (Eubank 2014) notes that delirium has a risk of death similar to that for myocardial infarction and severity of complications and costs similar to diabetes yet has never received the amount of attention that either of those 2 conditions have.
This is a welcome scoring system and we expect you’ll hear a lot more about its use in the next few years.
Some of our prior columns on delirium assessment and management:
· October 21, 2008 “Preventing Delirium”
· October 14, 2009 “Managing Delirium”
· February 10, 2009 “Sedation in the ICU: The Dexmedetomidine Study”
· March 31, 2009 “Screening Patients for Risk of Delirium”
· June 23, 2009 “More on Delirium in the ICU”
· January 26, 2010 “Preventing Postoperative Delirium”
· August 31, 2010 “”
· September 2011 “Modified HELP Helps Outcomes in Elderly Undergoing Abdominal Surgery”)
· December 2010 “The ABCDE Bundle”
· February 28, 2012 “AACN Practice Alert on Delirium in Critical Care”
· April 3, 2012 “New Risk for Postoperative Delirium: Obstructive Sleep Apnea”
· August 7, 2012 “Cognition, Post-Op Delirium, and Post-Op Outcomes”
· September 2013 “Disappointing Results in Delirium”
· October 29, 2013 “PAD: The Pain, Agitation, and Delirium Care Bundle”
· February 2014 “New Studies on Delirium”
· March 25, 2014 “Melatonin and Delirium”
Inouye SK, Kosar CM, Tommet D, et al. The CAM-S: Development and Validation of a New Scoring System for Delirium Severity in 2 Cohorts. Ann Intern Med 2014; 160(8):526-533
Eubank KJ, Covinsky KE. Delirium Severity in the Hospitalized Patient: Time to Pay Attention. Ann Intern Med 2014; 160(8): 574-575
A new review shows that over 5% of outpatients have errors in diagnosis, about half of which may be potentially harmful (Singh 2014). The authors estimate that such errors in diagnosis may affect 12 million outpatients annually in the US.
In our March 2013 What’s New in the Patient Safety World column “Diagnostic Error in Primary Care” and our January 2014 What’s New in the Patient Safety World column “Trigger Tools to Prevent Diagnostic Delays” we highlighted previous studies by Singh and colleagues (Singh 2013, Murphy 2014) on diagnostic errors in primary care that used trigger tool methodology. The new study included the previous work and a total of 3 studies using the same definitions and methodologies.
AHRQ, which partially funded the study, did a press release (AHRQ 2014) that also calls attention to the AHRQ toolkit “Improving Your Office Testing Process. A Toolkit for Rapid-Cycle Patient Safety and Quality Improvement” (see our October 2013 What’s New in the Patient Safety World column “New AHRQ Toolkit: Improving Your Office Testing”) and a new set of guides and interactive tools from the ONC to help health care providers more safely use electronic health information technology products, including test results reporting and follow up.
Diagnostic error, and specifically diagnostic error on the outpatient side, is finally receiving the attention it merits. It is important both in its frequent occurrence and the harm that results from it.
Some of our prior columns on diagnostic error:
Singh H, Meyer AND, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014; published online April 17, 2014
Singh H, Giardina TD, Meyer AND, et al. Types and Origins of Diagnostic Errors in Primary Care Settings. JAMA Intern Med 2013; published online February 25, 2013
Murphy DR, Laxmisan A, Reis BA, et al. Electronic health record-based triggers to detect potential delays in cancer diagnosis. BMJ Qual Saf 2014; 23: 8-16 Published Online First: 19 July 201
AHRQ. Diagnostic Errors Study Findings. Press Release April 16, 2014
AHRQ. Improving Your Office Testing Process. A Toolkit for Rapid-Cycle Patient Safety and Quality Improvement. August 2013
ONC (Office of the National Coordinator for Health Information Technology). Safer Guides. HealthIT.gov