Quality Patient Safety Indicators Research Articles

Risks of adverse events in colorectal patients: population-based study

BackgroundPostoperative (PO) outcomes are rapidly being integrated into value-based purchasing programs and associated penalties are slated for inclusion in the near future. Colorectal surgery procedures are extremely common and account for a high proportion of morbidity among general surgery. We sought to assess adverse events in colorectal surgical patients. MethodsWe performed a retrospective study using the Nationwide Inpatient Sample database, 2008–2012. Patients were identified using International Classification of Diseases, Ninth Revision, Clinical Modification codes and classified based on procedure indication: colon cancer, benign polyps, diverticulitis, inflammatory bowel disease, and ischemic colitis. The outcome of interest was inpatient adverse event identified by Agency for Healthcare Research and Quality's patient safety indicators (PSIs). ResultsWe identified 1,100,184 colorectal patients who underwent major surgery; 2.7% developed a PSI during their hospital stay. Compared to all colorectal patients, those with ischemic colitis had significantly higher risk-adjusted rates per 1000 case for pressure ulcer (50.20), failure to rescue (211.30), central line bloodstream infection (2.33) and PO DE/deep vein thrombosis (16.02), and sepsis (46.99), whereas benign polyps were associated with significantly lower risk-adjusted rates per 1000 cases for pressure ulcer (11.48), failure to rescue (84.79), central line bloodstream infection (0.97) and PO pulmonary embolism/deep vein thrombosis (4.81), and sepsis (11.23). Compared to both patient demographic and clinical characteristics, the procedure indication was the strongest predictor of any PSI relevant to colorectal surgery; patients with ischemic colitis had higher odds of experiencing a PSI (odds ratio, 1.84; 95% confidence interval, 1.71–1.99) compared with cancer patients. ConclusionsAmong colorectal surgery patients, inpatient events were not uncommon. We found important differential rates of adverse events by diagnostic category, with the highest odds ratio occurring in patients undergoing surgery for ischemic colitis. Our work elaborates the need for rigorous risk adjustment, quality improvement strategies for high-risk populations, and attention to detail in calculating financial incentives in emerging value-based purchasing programs.

Hospital-level Variation in the Quality of Benign Inpatient Urologic Surgery

To examine hospital-level variation in outcomes following benign urologic surgeries given that hospital-level variation in surgical outcomes can portend quality and appropriateness of care concerns and identify quality improvement opportunities in perioperative care. Using the Washington State Comprehensive Hospital Abstract Reporting System, we identified patients who underwent transurethral resection of the prostate (TURP), percutaneous nephrostolithotomy (PCNL), and pyeloplasty from 2003 to 2008. We classified prolonged postoperative length of stay (LOS) as that exceeding the 75th percentile, and we measured the rate of Agency for Healthcare Quality Patient Safety Indicators, readmissions, and death. We calculated hospital-specific observed-to-expected event rates using random effects multilevel multivariable models adjusted for age and comorbidity. We identified 6699 TURP patients at 54 hospitals, 2541 PCNL patients at 45 hospitals, and 584 pyeloplasty patients at 36 hospitals. Complication rates were highest after PCNL (22.9% prolonged LOS vs 17.3% for TURP and 13.9% for pyeloplasty, P < .001; 3.4% 90-day mortality vs 0.6% for TURP and 0% for pyeloplasty). Hospital-level variation was most substantial for LOS after TURP and pyeloplasty (8.1% and 14.3% of variance in prolonged LOS, respectively). Hospital-level variation is common after benign inpatient urologic surgeries and may relate to difference in perioperative provider practice patterns. The morbidity of PCNL in this study was higher than expected and merits further investigation.

Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) Are Associated With Increased Healthcare Utilization, Costs, and Mortality Among Patients Hospitalized With Acute Pancreatitis: Presidential Poster

Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) Are Associated With Increased Healthcare Utilization, Costs, and Mortality Among Patients Hospitalized With Acute Pancreatitis: Presidential Poster

Establishing Standard Performance Measures in Adult Traumatic Brain Injury Patients.

The Agency for Healthcare Research and Quality patient safety indicators (PSIs) and Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are publicly reported metrics that illustrate the overall quality of care provided at an institution. The national incidences of PSIs and HACs in traumatic brain injury (TBI) patients were estimated using the Nationwide Inpatient Sample database. To establish baseline incidences of PSIs and HACs among surgical TBI patients treated at nonfederal hospitals in the United States, and to identify patient factors contributing to these adverse events. The Nationwide Inpatient Sample database was queried for patients admitted with International Classification of Diseases, Ninth Revision diagnosis codes consistent with TBI between 2002 and 2011. The incidences of PSIs and HACs were estimated for TBI patients and evaluated for correlation with multiple factors, including comorbidity score, teaching hospital status, and insurance status. There were 15403 total PSIs among 24012 TBI patients. There were only 165 HACs among 24012 TBI patients. Only sepsis, deep vein thrombosis, and pressure ulcers occurred in more than 1% of patients. Patient age, sex, comorbidity score, and teaching hospital status were all found to significantly impact PSI incidence. Comorbidity score was found to significantly impact HAC incidence. Compared with private insurance, Medicaid patients developed significantly more HACs. These data may be used as reference values for hospitals reporting their own rates and seeking to improve the quality of care they provide for TBI patients.

Benchmarking statewide trauma mortality using Agency for Healthcare Research and Quality's patient safety indicators

BackgroundImproving clinical outcomes of trauma patients is a challenging problem at a statewide level, particularly if data from the state's registry are not publicly available. Promotion of optimal care throughout the state is not possible unless clinical benchmarks are available for comparison. Using publicly available administrative data from the State Department of Health and the Agency for Healthcare Research and Quality (AHRQ) patient safety indicators (PSIs), we sought to create a statewide method for benchmarking trauma mortality and at the same time also identifying a pattern of unique complications that have an independent influence on mortality. MethodsData for this study were obtained from State of Florida Agency for Health Care Administration. Adult trauma patients were identified as having International Classification of Disease ninth edition codes defined by the state. Multivariate logistic regression was used to create a predictive inpatient expected mortality model. The expected value of PSIs was created using the multivariate model and their beta coefficients provided by the AHRQ. Case-mix adjusted mortality results were reported as observed to expected (O/E) ratios to examine mortality, PSIs, failure to prevent complications, and failure to rescue from death. ResultsThere were 50,596 trauma patients evaluated during the study period. The overall fit of the expected mortality model was very strong at a c-statistic of 0.93. Twelve of 25 trauma centers had O/E ratios <1 or better than expected. Nine statewide PSIs had failure to prevent O/E ratios higher than expected. Five statewide PSIs had failure to rescue O/E ratios higher than expected. The PSI that had the strongest influence on trauma mortality for the state was PSI no. 9 or perioperative hemorrhage or hematoma. Mortality could be further substratified by PSI complications at the hospital level. ConclusionsAHRQ PSIs can have an integral role in an adjusted benchmarking method that screens at risk trauma centers in the state for higher than expected mortality. Stratifying mortality based on failure to prevent PSIs may identify areas of needed improvement at a statewide level.

Association between in-hospital adverse events and mortality for patients with brain tumors.

The Agency for Healthcare Research and Quality patient safety indicators (PSIs) and the Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are administrative data-based metrics. The use of these outcomes as standard performance measures has been discussed in previous studies. With the objective of determining the applicability of these events as performance metrics among patients undergoing brain tumor surgery, this study had 2 aims: 1) to evaluate the association between PSIs, HACs, and in-hospital mortality rates; and 2) to determine a correlation between hospital volume, PSIs, and HACs. Patients with brain tumors treated between 1998 and 2009 were captured in the Nationwide Inpatient Sample database. Hospitals were categorized into groups according to surgical volume. Associations between PSIs, HACs, and in-hospital mortality rates were studied. Factors associated with a PSI, HAC, and mortality were evaluated in a multivariate setting. A total of 444,751 patients with brain tumors underwent surgery in 1311 hospitals nationwide. Of these, 7.4% of patients experienced a PSI, 0.4% an HAC, and 1.9% died during their hospitalization. The occurrence of a PSI was strongly associated with mortality. Patients were 7.6 times more likely to die (adjusted odds ratio [aOR] 7.6, CI 6.7-8.7) with the occurrence of a PSI in a multivariate analysis. Moderate to strong associations were found between HACs, PSIs, and hospital volume. Patients treated at the highest-volume hospitals compared with the lowest-volume ones had reduced odds of a PSI (aOR 0.9, CI 0.8-1.0) and HAC (aOR 0.5, CI 0.5-0.08). Patient safety-related adverse events were strongly associated with in-hospital mortality. Moderate to strong correlations were found between PSIs, HACs, and hospital procedural volume. Patients treated at the highest-volume hospitals had consistently lower rates of mortality, PSIs, and HACs compared with those treated at the lowest-volume facilities.

Postoperative wound dehiscence: Predictors and associations.

The Agency for Healthcare Research and Quality patient safety indicators (PSI) were developed as a metric of hospital complication rates. PSI-14 measures postoperative wound dehiscence and specifically how often a surgical wound in the abdominal or pelvic area fails to heal after abdominopelvic surgery. Wound dehiscence is estimated to occur in 0.5-3.4% of abdominopelvic surgeries, and carries a mortality of up to 40%. Postoperative wound dehiscence has been adopted as a surrogate safety outcome measure as it impacts morbidity, length of stay, healthcare costs and readmission rates. Postoperative wound dehiscence cases from the Nationwide Inpatient Sample demonstrate 9.6% excess mortality, 9.4 days of excess hospitalization and $40,323 in excess hospital charges relative to matched controls. The purpose of the current study was to investigate the associations between PSI-14 and measurable medical and surgical comorbidities using the Explorys technology platform to query electronic health record data from a large hospital system serving a diverse patient population in the Washington, DC and Baltimore, MD metropolitan areas. The study population included 25,636 eligible patients who had undergone abdominopelvic surgery between January 1, 2008 and December 31, 2012. Of these cases, 786 (2.97%) had postoperative wound dehiscence. Patient-associated comorbidities were strongly associated with PSI-14, suggesting that this indicator may not solely be an indicator of hospital safety. There was a strong association between PSI-14 and opioid use after surgery and this finding merits further investigation.

Postoperative venous thromboembolism outcomes measure: analytic exploration of potential misclassification of hospital quality due to surveillance bias.

In Brief The Agency for Healthcare Research and Quality Patient Safety Indicator 12 Postoperative Venous Thromboembolism (VTE) does not accurately reflect true hospital quality because (1) current measure specifications preclude excluding nonclinically significant VTE from the measure numerator, and (2) not all patients in the measure denominator are at risk of VTE diagnosis.

The effect of a hospitalist comanagement service on vascular surgery inpatients

Vascular surgery patients have increased medical comorbidities that amplify the complexity of their care. We assessed the effect of a hospitalist comanagement service on inpatient vascular surgery outcomes. We divided 1059 patients into two cohorts for comparison: 515 between January 2012 and December 2012, before the implementation of a hospitalist comanagement service, and 544 between January 2013 and October 2013, after the initiation of a hospitalist comanagement service. Nine vascular surgeons and 10 hospitalists participated in the hospitalist comanagement service. End points measured were in-hospital mortality, length of stay (LOS), 30-day readmission rates, visual analog scale pain scores (0-10), inpatient adult safety assessments using the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators, and resident perceptions assessed by survey. The in-hospital mortality rate decreased from 1.75% to 0.37% after the implementation of the hospitalist comanagement service (P = .016), with a decrease in the observed-to-expected ratio from 0.89 to 0.22. The risk-adjusted in-hospital mortality decreased from 1.56% to 0.0008% (P = .003). Mean LOS was lower in the base period, at 5.1 days vs 5.5 days (P < .001), with an observed-to-expected ratio of 0.83 and 0.78, respectively. The risk-adjusted LOS increased from 4.2 days to 4.3 days (P < .001). The overall 30-day readmission rate was unchanged, at 23.1% compared with 22.8% (P = .6). The related 30-day readmission rate was also similar, at 11.5% compared with 11.4% (P = .5). Patients reporting no pain during hospitalization increased from 72.8% before the hospitalist comanagement service to 77.8% after (P = .04). Reports of moderate pain decreased from 14% to 9.6% (P = .016). Mild and severe pain scores were similar between the two groups. Adult safety measured by AHRQ demonstrated a decrease from three to zero patients in the number of deaths among surgical patients with treatable complications (P = .04). Most house staff reported that the comanagement program had a positive effect on patient care and education. The hospitalist comanagement service has resulted in a significant decrease in in-hospital mortality rates, patient safety, as measured by AHRQ, and improved pain scores. Resident surveys demonstrated perceived improvement in patient care and education. Continued observation will be necessary to assess the long-term effect of the hospitalist comanagement service on quality metrics.

Health disparities and stroke: the influence of insurance status on the prevalence of patient safety indicators and hospital-acquired conditions.

The Agency for Healthcare Research and Quality patient safety indicators (PSIs) and the Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are publicly reported quality metrics linked directly to reimbursement. The occurrence of PSIs and HACs is associated with increased mortality and hospital costs after stroke. The relationship between insurance status and PSI and HAC rates in hospitalized patients treated for acute ischemic stroke was determined using the Nationwide Inpatient Sample (NIS) database. The NIS was queried for all hospitalizations involving acute ischemic stroke between 2002 and 2011. The rate of each PSI and HAC was determined by searching the hospital records for ICD-9 codes. The SAS statistical software package was used to calculate rates and perform multivariable analyses to determine the effects of patient variables on the probability of developing each indicator. The NIS query revealed 1,507,336 separate patient admissions that had information on both primary payer and hospital teaching status. There were 227,676 PSIs (15.1% of admissions) and 42,841 HACs reported (2.8%). Patient safety indicators occurred more frequently in Medicaid/self-pay/no-charge patients (19.1%) and Medicare patients (15.0%) than in those with private insurance (13.6%; p < 0.0001). In a multivariable analysis, Medicaid, self-pay, or nocharge patients had significantly longer hospital stays, higher mortality, and worse outcomes than those with private insurance (p < 0.0001). Insurance status is an independent predictor of patient safety events after stroke. Private insurance is associated with lower mortality, shorter lengths of stay, and improved clinical outcomes.

ED-08 * INSURANCE STATUS INFLUENCES THE RATES OF REPORTABLE QUALITY METRICS IN BRAIN TUMOR PATIENTS: A NATIONWIDE INPATIENT SAMPLE STUDY

INTRODUCTION: The Patient Protection and Affordable Care Act was passed to narrow disparities and improve the quality of health care in the United States. The Agency for Healthcare Research and Quality patient safety indicators (PSIs) and the Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are quality metrics tracked in healthcare institutions. The effects of insurance status on PSIs and HACs were analyzed using the Nationwide Inpatient Sample (NIS) database for brain tumor patients. METHODS: The NIS was queried for hospitalizations between 2002 and 2011 involving brain tumors patients. The incidence for each PSI and HAC was determined by ICD-9 codes. The independent variable was primary payer status. Because of the age restriction with Medicare, comparisons were made between Medicaid/self-pay and private insurance. Logistic regression modeling was used to estimate the effects of insurance status on individual PSIs, HACs, and patient outcomes. RESULTS: Overall, 113,797 PSIs and 15,810 HACs occurred in 548,727 brain tumor patients, an estimated national incidence of 20.7% and 2.9% respectively. Medicaid/self-pay patients had a higher PSI incidence compared to private insurance patients (20.6% versus 18.6%). Medicaid/self-pay patients also had a higher incidence of HACs (2.2% versus 1.9%). After controlling for patient and hospital factors, Medicaid/self-pay patients were estimated to experience 3.6% more PSIs per patient than private insurance (p = 0.0042). In addition, Medicaid/self-pay patients were eistamted to experience 12.6% more HACs per patient (p = 0.0001). The greater incidence of PSIs and HACs correlated with increased length of stay and worse discharge outcomes and in-hospital mortality in the Medicaid/self-pay patient population. CONCLUSIONS: Significant variability exists in the incidence of PSIs and HACs in brain tumor patients based on insurance status. The cause of these differences should be studied prospectively to begin the process of improving quality metrics in vulnerable patient populations.

Patient Safety in Surgical Care

The 1999 report of the Institute of Medicine, To Err Is Human: Building a Safer Health System, made national headlines with its estimates of the frequency and severity of adverse events in health care, including that as many as 98,000 medical error–related deaths occur each year in the United States. The observation that the basic principles of human error are highly applicable to clinical practice has markedly advanced our understanding and willingness to address error in this setting. This review seeks to address the characteristics of systems in general and the system of surgical care in particular. It describes the growing knowledge of factors that affect human performance and how these factors contribute to adverse surgical outcomes. The review also outlines current obstacles to improving safety, identifies systems approaches to making improvements, and discusses ways in which surgeons can take the lead in overcoming these obstacles. An overall goal is that acceptance of error and a willingness to investigate its underlying causes will allow health care professionals to make use of the lessons learned from study of nonmedical systems. Tables include definitions of terms related to patient safety, the operation profile, handoff coordination and communication objectives and relevant strategies, nonmedical system techniques applicable to medical systems, Agency for Healthcare Quality and Research patient safety indicators, National Quality Forum list of health care facility–related serious reportable events, and examples of surgically relevant quality improvement practices appropriate for widespread implementation. Figures include the Swiss Cheese Model representing the relationship between latent and active errors and adverse outcomes, a schematic depiction of the process by which system failures may lead to injury, the Systems Engineering in Patient Safety Model of work system and patient safety, and a depiction of contrasting characteristics of medical practice in the 20th and 21st centuries. This review contains 4 figures, 7 tables, and 165 references.

Challenges and remediation for Patient Safety Indicators in the transition to ICD-10-CM.

Reporting of hospital adverse events relies on Patient Safety Indicators (PSIs) using International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes. The US transition to ICD-10-CM in 2015 could result in erroneous comparisons of PSIs. Using the General Equivalent Mappings (GEMs), we compared the accuracy of ICD-9-CM coded PSIs against recommended ICD-10-CM codes from the Centers for Medicaid/Medicare Services (CMS). We further predict their impact in a cohort of 38 644 patients (1 446 581 visits and 399 hospitals). We compared the predicted results to the published PSI related ICD-10-CM diagnosis codes. We provide the first report of substantial hospital safety reporting errors with five direct comparisons from the 23 types of PSIs (transfusion and anesthesia related PSIs). One PSI was excluded from the comparison between code sets due to reorganization, while 15 additional PSIs were inaccurate to a lesser degree due to the complexity of the coding translation. The ICD-10-CM translations proposed by CMS pose impending risks for (1) comparing safety incidents, (2) inflating the number of PSIs, and (3) increasing the variability of calculations attributable to the abundance of coding system translations. Ethical organizations addressing ‘data-, process-, and system-focused’ improvements could be penalized using the new ICD-10-CM Agency for Healthcare Research and Quality PSIs because of apparent increases in PSIs bearing the same PSI identifier and label, yet calculated differently. Here we investigate which PSIs would reliably transition between ICD-9-CM and ICD-10-CM, and those at risk of under-reporting and over-reporting adverse events while the frequency of these adverse events remain unchanged.

Agency for Healthcare Research and Quality Patient Safety Indicators and Mortality in Surgical Patients

Patient safety indicators (PSI), developed by the Agency for Healthcare Research and Quality, use administrative billing data to measure and compare patient safety events at medical centers. We retrospectively examined whether PSIs accurately reflect patients' risk of mortality, hospital length of stay, and intensive care unit (ICU) requirements at an academic medical center. Surgical patient records with PSIs were reviewed between October 2011 and September 2012 at our urban academic medical center. Primary outcomes studied included mortality, hospital length of stay, and ICU requirements. Subset analysis was performed for each PSI and its association with the outcome measures. PSIs were more common among surgical patients who died as compared with those alive at discharge (35.3 vs 2.7 PSIs/100 patients, P < 0.01). Although patients who died with PSIs had shorter hospital courses, they had a significantly greater ICU requirement than those without a PSI (96.0 vs 61.1%, P < 0.01) and patients who were alive at discharge (96.0 vs 48.0%, P < 0.01). The most frequently associated PSIs with mortality were postoperative metabolic derangements (41.7%), postoperative sepsis (38.5%), and pressure ulcers (33.3%). PSIs occur at a higher frequency in surgical patients who die and are associated with increased ICU requirements.

Diffusion of surgical innovations, patient safety, and minimally invasive radical prostatectomy.

Surgical innovations disseminate in the absence of coordinated systems to ensure their safe integration into clinical practice, potentially exposing patients to increased risk for medical error. To investigate associations of patient safety with the diffusion of minimally invasive radical prostatectomy (MIRP) resulting from the development of the da Vinci robot. A cohort study of 401 325 patients in the Nationwide Inpatient Sample who underwent radical prostatectomy during MIRP diffusion between January 1, 2003, and December 31, 2009. We used Agency for Healthcare Research and Quality Patient Safety Indicators (PSIs), which measure processes of care and surgical provider performance. We estimated the prevalence of MIRP among all prostatectomies and compared PSI incidence between MIRP and open radical prostatectomy in each year during the study. We also collected estimates of MIRP incidence attributed to the manufacturer of the da Vinci robot. Patients who underwent MIRP were more likely to be white (P = .004), have fewer comorbidities (P = .02), and have undergone surgery in higher-income areas (P = .005). The incidence of MIRP was substantially lower than da Vinci manufacturer estimates. Rapid diffusion onset occurred in 2006, when MIRP accounted for 10.4% (95% CI, 10.2-10.7) of all radical prostatectomies in the United States. In 2005, MIRP was associated with an increased adjusted risk for any PSI (adjusted odds ratio, 2.0; 95% CI, 1.1-3.7; P = .02) vs open radical prostatectomy. Stratification by hospital status demonstrated similar patterns: rapid diffusion onset among teaching hospitals occurred in 2006 (11.7%; 95% CI, 11.3-12.0), with an increased risk for PSI for MIRP in 2005 (adjusted odds ratio, 2.7; 95% CI, 1.4-5.3; P = .004), and onset among nonteaching hospitals occurred in 2008 (27.1%; 95% CI, 26.6-27.7), with an increased but nonsignificant risk for PSI in 2007 (adjusted odds ratio, 2.0; 95% CI, 0.8-5.2; P = .14). During its initial national diffusion, MIRP was associated with diminished perioperative patient safety. To promote safety and protect patients, the processes by which surgical innovations disseminate into clinical practice require refinement.

123 Establishing Standard Performance Measures in Adult Traumatic Brain Injury Patients

INTRODUCTION:: The Agency for Healthcare Research and Quality (AHRQ) patient safety indicators (PSIs) and Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are publicly reported metrics that illustrate the overall quality of care of an institution. The incidences of PSIs and HACs in traumatic brain injury (TBI) patients were determined using the nationwide inpatient sample (NIS) database. METHODS:: The NIS database was queried for patients admitted with International Classification of Diseases, Ninth Revision diagnosis codes consistent with TBI. The incidences of PSIs and HACs were determined for TBI patients and compared between teaching and non-teaching hospitals. A logistic regression model was created to estimate the effects of insurance status on PSIs, HACs, and patient outcomes. RESULTS:: There were 15 403 total PSIs among 24 012 TBI patients. Teaching hospitals had a higher incidence of PSIs (653 per 1000 patients) compared to non-teaching hospitals (615 per 1000 patients; P =.0016) as well as a higher fraction of patients suffering at least 1 PSI (54.0% vs 50.8%; P =.004). There were only 165 HACs among 24 012 TBI patients. There was no significant difference between teaching and non-teaching hospitals in incidence of HACs; HACs were more common, however, in Medicaid patients compared to privately insured patients (8.9 vs 5.0 HACs/1000 patients; P =.0015.) Multivariate analyses comparing the Medicaid and private insurance patients demonstrated no difference between the groups in either poor outcome or mortality; however, the length of stay for Medicaid patients was prolonged 2.0 days compared with private insurance (95% CI: [1.35, 2.62]; P CONCLUSION:: Clinical outcomes following TBI were not affected by insurance status. Patients treated in teaching hospitals suffered a higher incidence of PSIs, although this was not controlled for injury severity. These data may be used as reference values for hospitals reporting their own rates and seeking to improve the quality of care they provide for TBI patients. Language: en

Concurrent Surgical Patient Safety Indicator Evaluation Results in More Accurate Reporting and Reimbursement

We sought to determine whether concurrent (before discharge) Agency for Healthcare Research and Quality patient safety indicator evaluation would result in a more expeditious review, accurate reporting, and improved reimbursement. We compared the period of preconcurrent (preC) coding (January 2012 to June 2012) with the period after concurrent coding (postC) began (July 2012 to December 2012) for total billing errors. There were 276 records reviewed in the preC versus 424 in the postC time periods. Overall coding errors were 225 (81.5%) preC versus 365 (86.1%) postC (P = nonsignificant), whereas documentation errors were present in 26 (9.4%) preC versus 40 (9.4%) postC (P = nonsignificant). Total charges were $3,782,024 preC and $2,011,144 postC. Recodes requiring rebilling were 21 (7.6%) preC for a total of $213,723 rebilled versus four (0.9%) postC for a total of $31,327 rebilled (P < 0.0001). Time from service to review was 98.7 preC versus 52.3 postC days (P < 0.0001). Time from service until rebill submitted averaged 100.8 preC versus 54.0 postC days (P = 0.06). Concurrent review allows for more accurate reporting because recodes are completed before discharge. Billing delays prolong time to reimbursement and results in loss of revenue.

The effect of health information technology implementation in Veterans Health Administration hospitals on patient outcomes

BackgroundThe impact of health information technology (HIT) in hospitals is dependent in large part on how it is used by nurses. This study examines the impact of HIT on the quality of care in hospitals in the Veterans Health Administration (VA), focusing on nurse-sensitive outcomes from 1995 to 2005. MethodsData were obtained from VA databases and original data collection. Fixed-effects Poisson regression was used, with the dependent variables measured using the Agency for Healthcare Research and Quality Inpatient Quality Indicators and Patient Safety Indicators software. Dummy variables indicated when each facility began and completed implementation of each type of HIT. Other explanatory variables included hospital volume, patient characteristics, nurse characteristics, and a quadratic time trend. ResultsThe start of computerized patient record implementation was associated with significantly lower mortality for two diagnoses but significantly higher pressure ulcer rates, and full implementation was associated with significantly more hospital-acquired infections. The start of bar-code medication administration implementation was linked to significantly lower mortality for one diagnosis, but full implementation was not linked to any change in patient outcomes. ConclusionsThe commencement of HIT implementation had mixed effects on patient outcomes, and the completion of implementation had little or no effect on outcomes. ImplicationsThis longitudinal study provides little support for the perception of VA staff and leaders that HIT has improved mortality rates or nurse-sensitive patient outcomes. Future research should examine patient outcomes associated with specific care processes affected by HIT.

Impact of Including Readmissions for Qualifying Events in the Patient Safety Indicators

The Agency for Healthcare Research and Quality Patient Safety Indicators (PSIs) do not capture complications arising after discharge. This study sought to quantify the bias related to omission of readmissions for PSI-qualifying conditions. Using 2000-2009 California Office of Statewide Health Planning and Development Patient Discharge Data, the study team examined the change in PSI rates when including readmissions in the numerator, hospitals performing in the extreme deciles, and longitudinal performance. Including 7-day readmissions resulted in a 0.3% to 8.9% increase in average hospital PSI rates. Hospital PSI rates with and without PSI-qualifying 30-day readmissions were highly correlated for point estimates and within-hospital longitudinal change. Most hospitals remained in the same relative performance decile. Longer length of stay, public payer, and discharge to skilled nursing facilities were associated with a higher risk of readmission for a PSI-qualifying event. Failure to include readmissions in calculating PSIs is unlikely to lead to erroneous conclusions.

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Introduction: Severe Sepsis (SS) and acute kidney injury (AKI) are costly postoperative complications that frequently occur together. Cost burden of SS has been extensively studied; the increased cost of concurrent SS and AKI less so. Methods: This was a retrospective cohort study of 50,314 adult patients with previously normal renal function undergoing surgical procedures requiring > 48 hours admission. SS was defined using the Agency for Healthcare Research and Quality (AHRQ) patient safety indicators "Postoperative Sepsis". Risk, Injury, Failure, Loss, and End-stage Kidney (RIFLE) consensus criteria and serial serum creatinine measurements were used to define AKI. We modeled the primary outcome variable, inflation adjusted hospital cost, as a function of common postoperative complications. All models were adjusted for hospital stay characteristics and validated using validation datasets. Adjusted costs for patients with and without each postoperative complication and 95% confidence intervals were calculated for each. Hospital cost was log transformed due to skewness. Smearing estimates were used to correct for bias when estimates were back-transformed. Results: Average cost per patient with no complications was $15,600 (95%CI: $15,500-$15,700). AKI occurred in 35% of patients with average cost per patient of $24,300 (95%CI: $23,900, 24,600); SS 4.8% with average cost per patient of $31,200 (95%CI: $24,700, $37,700). 94% of patients with SS had concurrent AKI with average cost per patient of $54,500 (95%CI: $43,600-$65,400). Additional postoperative complications occurred more frequently in patients with AKI and SS; each with an average cost increase of 8 to 9 times that of patients without AKI and SS. The most common and costly were ICU admission greater than 2 days (95%, $180,100 95% CI: $174,300, $186,000) and mechanical ventilation > 2 days (82%, $198,900 95% CI: $192.900, $205,000). Conclusions: Hospital costs of concurrent SS and AKI are higher than in any other subset of patients adjusting for inflation, patient demographics and clinical characteristics. AKI is a common complication in patients with SS and contributes to increased resource utilization and cost. Whether the use of treatment protocols for SS decreases occurrence of AKI and subsequently reduces the cost requires further investigation.