A multidisciplinary team was organized to optimize care and enhance compliance in a comprehensive cancer emergency center. We performed a 4-phase study, three of which were interventional: intense education regarding PCM; microbiologic analysis of the pathogens responsible for the pneumonias; development and implementation of an institutional pneumonia algorithm and order-set.
In phase-4, we analyzed five PCMs. We identified a gap between our patient population and some PCMs which relates to antibiotics selection.
The treatment of cancer patients and pneumonia falls outside established guidelines for treating community-acquired pneumonia. Although the algorithm and order set implemented optimized care and minimized variation, national benchmarks for four of the PCMs were not met. Our findings provide information for policymakers considering pneumonia measurements for antibiotic selection in a cancer care setting. To measure the performance of healthcare organizations in delivering high-quality care to patients with pneumonia, The Joint Commission developed Pneumonia Core Measures PCM , which are reportable evidence-based standards of practice.
The measurements themselves can be used in other areas of accreditation, expanded to public reporting and pay-for-performance initiatives Bielanski, PN 3a: Blood cultures performed within 24 hours prior to or 24 hours after hospital arrival for patients who were transferred or admitted to the intensive care unit ICU within 24 hours of hospital arrival.
PN 3b: Blood cultures performed in the emergency department prior to initial antibiotic received in hospital. While specialty hospitals are currently exempt from this initiative, they will begin the process of public reporting for the first time in To prepare for future reporting we have elected to begin reporting the PCM.
Three major areas of clinical gaps and improvement were identified.
Scott cr1 pro 2009 specifications manual for national hospital inpatient
Most of our patients have healthcare-associated pneumonia HCAP , vs. These areas were tackled in three different phases by implementing quality tools; the quality model for improvement used was the Plan, Do, Study, Act PDSA cycle, and phase-4 was conducted to evaluate our performance and compare it to national benchmarks. We present our experience here.
A multidisciplinary team was organized into a Pneumonia Team. This team was charged with evaluating, analyzing and disseminating data; developing, implementing and monitoring the pneumonia algorithm and order set; and educating all clinical staff throughout the institution. The present study included patients 18 years and older who were identified with a diagnosis of pneumonia based on the International Classification of Diseases Ninth Revision ICD A record review of those cases was done confirming the documentation of symptoms of an acute lower respiratory tract illness, and new infiltrate as detected using chest radiography or computed tomography at the time of admission to the EC.
The study also included patients with acute respiratory measure and septicemia with a secondary diagnosis of pneumonia.
HCAP in our patients is defined as a patient who, in addition to the pneumonia inclusion criteria, met any of the following criteria: hospitalization for two or more days within 90 days, residence in a nursing home or extended care facility, home infusion therapy including antibiotic infusion therapy , chronic dialysis within 30 days, home wound care, a family member with a multidrug-resistant pathogen L.
Mandell et al. Reviewers were in agreement regarding questionable elements, and business rules for standardization. The quality model for improvement used was PDSA cycle.
To analyze the potential causes of poor compliance with PCM, a cause and effect analysis was developed that highlighted the major categories such as blood culture and timing of antibiotics. The analysis revealed that the EC staff were not aware of the importance of achieving the quality indicators for pneumonia.
This was because our institution was Prospective Payment System exempt and we were not required to report core measures. We also made a process map to track patients from their initial arrival to the EC through their disposition.
2015 Scott CR1 Road Bike Review and Specs
We identified areas needing improvement and implemented the following interventions:. A cup for sputum collection is given during triage or in the room, to facilitate collection. The automated medication dispensing system was programmed to remind nurses to draw cultures before administering antibiotics. The strategy for implementing these interventions consisted of an intensive education campaign.
Assessing Compliance with Established Pneumonia Core Measures at a Comprehensive Cancer Center
Education was broad and targeted administrators and all clinical staff, including phlebotomists, ER consultants, and unit clerks. The educational methods used included one to one contact, presentations during scheduled ER staff meetings, and e-mails; posters were placed in different areas of the ER, brochures were created and distributed to all.
The intervention phase lasted one month.
We collected post-intervention data on each of the 85 pneumonia patients who presented in November and December of The objective of this phase was to identify the microbiology and empiric antibiotics for EC cancer patients with pneumonia. We developed a data collection tool to retrospectively review the microbiology, malignancy and pneumonia types. Data from 85 pneumonia patients in November and December of and from pneumonia patients in May, June, and July of were collected. The objective of this phase was to deploy an institutional pneumonia algorithm and order set.
The institution adopted the algorithm and order set as best practice of care in December Online Figure 1.
In February , the pneumonia order set became available in the EMR for institution-wide use. The strategy to initiate change consisted of intensive education initially and then yearly before the pneumonia season.
After the pneumonia order set was implemented, we collected the PCM data of patients with pneumonia who presented to the EC in February, October, and December of To determine the pneumonia order set was used correctly, we analyzed the data of patients with pneumonia who presented to the EC during two randomly selected weeks in each month in October and February We used Minitab as the statistical program for control chart analysis.
The objective of this phase was to determine whether the institution was in compliance with PCM as recommended by the National Hospital Quality Measures.
The microbiology data was also collected and analyzed during this phase. Top performance and benchmarks are calculated quarterly based on the UHC members rates. The data were collected for each phase contemporaneously, utilizing the hospital data of patients seen in the EC as mentioned previously. The denominator used to calculate percentage was the number of all patients who qualified as having a diagnosis of pneumonia and who met all inclusion criteria.
Main outcome measures included the percentage of patients who met the PCM evaluated in phases-1 and 4. A standard control chart p-chart was used for phase-3 analysis looking at the correct use of recommended antibiotics as per pneumonia order set. The phase-4 data were analyzed by quarter-year result. The UHC programs the specifications into the database automatically and provides the data exchange directly to Quality Net.
We also evaluated the reasons for failing to meet benchmarks for the data collected in phase We identified EC patients who had pneumonia. Ninety-three patients At that time, The percentage frequency of sputum cultures increased from Ninety-four percent of EC patients received antibiotics Table 1.
The organisms isolated most often from the respiratory specimens of patients with solid tumors were Pseudomonas species, Stenotrophomonas maltophilia , Streptococcus species, and Staphylococcus aureus. A similar pattern was seen in patients with HM including resistant organisms.
In patients who had positive blood and respiratory cultures, the blood culture isolates were often different from the respiratory specimen isolates. Candida species, but no molds, were isolated from patients with solid tumors, whereas molds mainly Aspergillus species were isolated from patients with HMs.
Two patients had mycobacterial infections, and one patient had Pneumocystis pneumonia. The phase 2 contributed to identifying local microbiology affecting our patient population and to direct antimicrobial treatment. We have achieved the main goal of optimizing care in patients with pneumonia; however it did not translate into compliance with all PCM.
Some of the antibiotics administered did not fall under recommended antibiotics by CMS measurements. We describe a multiyear, multidisciplinary process of quality improvement initiatives aimed at improving our compliance with PCM and care of cancer patients who present to a comprehensive cancer center with pneumonia.
In phase-1 of the study, our compliance with established pneumonia performance indicators improved significantly after quality improvement initiatives were implemented.
We significantly improved on the rate for obtaining sputum cultures in producers. The PCMs for antibiotic timing have changed since phase-1 was completed. These improvements were sustained after the algorithm and order set was implemented.
In phase-2, we found that the organisms that cause CAP in other patient populations e. Both multidrug-resistant and extended-spectrum beta-lactamase pathogens were identified in HCAP patients; consequently, empiric antibiotic therapy options for cancer patients who present with pneumonia may vary considerably from those mentioned in various guidelines Lionel A.
Ideally, empiric antibiotic therapy for such patients should be based on local microbiologic and susceptibility data. The Pneumonia Team found that meeting the current PCM does not necessarily translate into optimal care for cancer patients.
Most of the patients in the present study met the clinical and microbiologic criteria for HCAP; therefore, CAP patients comprised a small portion of the study population. The pneumonia algorithm and order set was implemented to standardize, minimize variation and to match the care needs of our patient population and the framework to improve outcome.
A previous study has demonstrated that the use of a standardized pneumonia order set can improve PCM compliance and reduce in-hospital mortality Mandell et al, In phase-3, we observed an improvement in the utilization of the pneumonia order set.
We believe intense and yearly education was the main factor driving this gain. In those instances, the denominator used to calculate the compliance rates represented a considerably small sample of the study population.
This difference may be due to the fact that MD Anderson is a comprehensive cancer center whose patient population is markedly different from that of non-specialty hospitals. However, antibiotic treatment was given in accordance with institutional and local guidelines. Similar findings regarding non-adherence to recommended antibiotics by CMS was observed in patients with pneumonia admitted to ICU and in those with previous methicillin-resistant Staphylococcus aureus infection in other study Shahian et al.
When looking at the specific PCM, analysis of the poor compliance with PN-3b revealed that in some cases blood cultures were collected a few minutes after antibiotics were administered. Patients who arrive late in the evening to the EC and are treated and then admitted as inpatients the next morning, had their admission date entered in the UHC as the day after EC treatment. Thus, it appears that all blood tests, imaging studies, and antibiotic administrations were performed before the patient arrived at the hospital.
We found this to be a system issue with timing of procedures. Our study was not without potential limitations. One potential limitation was the variability of the chart reviewers; four different reviewers abstracted pneumonia data from EMR, which may have led to inconsistencies in the way the data were abstracted. However, we were confident that, owing to prior discussions and collaboration among the reviewers, their abstracted data would be of similar standards and accuracy, and we performed a validation study that confirmed this finding.
Another potential limitation was the lack of comprehensive EMR, which made data abstraction a time-consuming endeavor. The reviewers had to retrieve various documents to find the most accurate data. The exact timing of antibiotics administration could be inaccurate owing to a lack of medication scanning process prior to medication administration.