Video-Assisted vs Robotic-Assisted Lung Lobectomies for Operating Room Resource Utilization and Patient Outcomes.

Importance: With increased use of robots, there is an inadequate understanding of minimally invasive modalities' time costs. This study evaluates the operative durations of robotic-assisted vs video-assisted lung lobectomies. Objective: To compare resource utilization, specifically operative time, between video-assisted and robotic-assisted thoracoscopic lung lobectomies. Design, Setting, and Participants: This retrospective cohort study evaluated patients aged 18 to 90 years who underwent minimally invasive (robotic-assisted or video-assisted) lung lobectomy from January 1, 2020, to December 31, 2022, with 90 days' follow-up after surgery. The study included multicenter electronic health record data from 21 hospitals within an integrated health care system in Northern California. Thoracic surgery was regionalized to 4 centers with 14 board-certified general thoracic surgeons. Exposures: Robotic-assisted or video-assisted lung lobectomy. Main Outcomes and Measures: The primary outcome was operative duration (cut to close) in minutes. Secondary outcomes were length of stay, 30-day readmission, and 90-day mortality. Comparisons between video-assisted and robotic-assisted lobectomies were generated using the Wilcoxon rank sum test for continuous variables and the χ2 test for categorical variables. The average treatment effects were estimated with augmented inverse probability treatment weighting (AIPTW). Patient and surgeon covariates were adjusted for and included patient demographics, comorbidities, and case complexity (age, sex, race and ethnicity, neighborhood deprivation index, body mass index, Charlson Comorbidity Index score, nonelective hospitalizations, emergency department visits, a validated laboratory derangement score, a validated institutional comorbidity score, a surgeon-designated complexity indicator, and a procedural code count), and a primary surgeon-specific indicator. Results: The study included 1088 patients (median age, 70.1 years [IQR, 63.3-75.8 years]; 704 [64.7%] female), of whom 446 (41.0%) underwent robotic-assisted and 642 (59.0%) underwent video-assisted lobectomy. The median unadjusted operative duration was 172.0 minutes (IQR, 128.0-226.0 minutes). After AIPTW, there was less than a 10% difference in all covariates between groups, and operative duration was a median 20.6 minutes (95% CI, 12.9-28.2 minutes; P < .001) longer for robotic-assisted compared with video-assisted lobectomies. There was no difference in adjusted secondary patient outcomes, specifically for length of stay (0.3 days; 95% CI, -0.3 to 0.8 days; P = .11) or risk of 30-day readmission (adjusted odds ratio, 1.29; 95% CI, 0.84-1.98; P = .13). The unadjusted 90-day mortality rate (1.3% [n = 14]) was too low for the AIPTW modeling process. Conclusions and Relevance: In this cohort study, there was no difference in patient outcomes between modalities, but operative duration was longer in robotic-assisted compared with video-assisted lung lobectomy. Given that this elevated operative duration is additive when applied systematically, increased consideration of appropriate patient selection for robotic-assisted lung lobectomy is needed to improve resource utilization.

Hospital-associated venous thromboembolism prophylaxis use by risk assessment at a large integrated health care network in Northern California.

BACKGROUND: Hospital-acquired venous thromboembolism (HA VTE) is a preventable complication in hospitalized patients. OBJECTIVE: We aimed to examine the use of pharmacologic prophylaxis (pPPX) and compare two risk assessment methods for HA VTE: a retrospective electronic Padua Score (ePaduaKP) and admitting clinician's choice of risk within the admission orderset (low, moderate, or high). DESIGN, SETTINGS AND PARTICIPANTS: We retrospectively analyzed prophylaxis orders for adult medical admissions (2013-2019) at Kaiser Permanente Northern California, excluding surgical and ICU patients. INTERVENTION: ePaduaKP was calculated for all admissions. For a subset of these admissions, clinician-assigned HA VTE risk was extracted. MAIN OUTCOME AND MEASURES: Descriptive pPPX utilization rates between ePaduaKP and clinician-assigned risk as well as concordance between ePaduaKP and clinician-assigned risk. RESULTS: Among 849,059 encounters, 82.2% were classified as low risk by ePaduaKP, with 42.3% receiving pPPX. In the subset with clinician-assigned risk (608,512 encounters), low and high ePaduaKP encounters were classified as moderate risk in 87.5% and 92.0% of encounters, respectively. Overall, 56.7% of encounters with moderate clinician-assigned risk received pPPX, compared to 7.2% of encounters with low clinician-assigned risk. pPPX use occurred in a large portion of low ePaduaKP risk encounters. Clinicians frequently assigned moderate risk to encounters at admission irrespective of their ePaduaKP risk when retrospectively examined. We hypothesize that the current orderset design may have negatively influenced clinician-assigned risk choice as well as pPPX utilization. Future work should explore optimizing pPPX for high-risk patients only.

Assessment of specificity of dermatopathologic criteria for IgG4-related skin disease.

BACKGROUND: IgG4-related disease (IgG4-RD) represents a recently characterized multisystemic fibroinflammatory condition that can manifest a spectrum of skin findings (IgG4-related skin disease; IgG4-RSD). Histopathologic and immunohistochemical criteria have been proposed; however, the specificity of these criteria merits scrutiny given the potential histopathologic overlap of IgG4-RSD and both neoplastic and inflammatory skin conditions featuring lymphoplasmacytic infiltrates (IgG4-RSD mimics). This study sought to assess the specificity of the criteria by quantifying the frequency by which an expanded spectrum of IgG4-RSD mimics meet proposed thresholds. METHODS: Following IRB approval, a total of 69 cases of IgG4-RD mimics, representing 14 different diagnoses featuring plasma cells, were reviewed and analyzed for the following histopathologic and immunohistochemical features: (i) maximum IgG4+ count/high-powered field (hpf) >200; (ii) IgG4/IgG ratio >0.4 averaged over 3 hpfs; (iii) IgG4+ count >10 per hpf. RESULTS: Screening for IgG4-RSD by histopathologic criteria demonstrated the high frequency of lymphoplasmacytic infiltrates, contrasted with the rarity of storiform fibrosis (only one case of erythema elevatum diutinum [EED]) and obliterative phlebitis (0 cases). By immunohistochemical criteria, the analysis revealed that no cases exceeded 200 IgG4+ cells; 13% (9/69) cases demonstrated an IgG4/IgG ratio of >0.4 averaged over 3 hpfs; and 23% (16/69) cases demonstrated a mean IgG4+ count of >10 per hpf. CONCLUSION: Application of proposed IgG4-RSD histopathologic criteria to an expanded spectrum of potential IgG4-RSD mimics (to include cutaneous marginal zone lymphoma, syphilis, necrobiosis lipoidica, lichen sclerosus, ALHE, psoriasis, lymphoplasmacytic plaque, EED, and erosive pustular dermatosis), highlights the relative nonspecificity of lymphoplasmacytic infiltrates contrasted with the stringency of storiform fibrosis and obliterative fibrosis. Furthermore, an IgG4+ cell count of >10 per hpf and an IgG4/IgG ratio of >0.4 are not specific to IgG4-RSD alone. In the appropriate clinical context for IgG4-RSD, histopathologic features still represent the entry threshold for diagnosis consideration, which then allows for further screening by immunohistochemical criteria.

Association Between Preoperative Hemodialysis Timing and Postoperative Mortality in Patients With End-stage Kidney Disease.

Importance: For patients with end-stage kidney disease treated with hemodialysis, the optimal timing of hemodialysis prior to elective surgical procedures is unknown. Objective: To assess whether a longer interval between hemodialysis and subsequent surgery is associated with higher postoperative mortality in patients with end-stage kidney disease treated with hemodialysis. Design, Setting, and Participants: Retrospective cohort study of 1 147 846 procedures among 346 828 Medicare beneficiaries with end-stage kidney disease treated with hemodialysis who underwent surgical procedures between January 1, 2011, and September 30, 2018. Follow-up ended on December 31, 2018. Exposures: One-, two-, or three-day intervals between the most recent hemodialysis treatment and the surgical procedure. Hemodialysis on the day of the surgical procedure vs no hemodialysis on the day of the surgical procedure. Main Outcomes and Measures: The primary outcome was 90-day postoperative mortality. The relationship between the dialysis-to-procedure interval and the primary outcome was modeled using a Cox proportional hazards model. Results: Of the 1 147 846 surgical procedures among 346 828 patients (median age, 65 years [IQR, 56-73 years]; 495 126 procedures [43.1%] in female patients), 750 163 (65.4%) were performed when the last hemodialysis session occurred 1 day prior to surgery, 285 939 (24.9%) when the last hemodialysis session occurred 2 days prior to surgery, and 111 744 (9.7%) when the last hemodialysis session occurred 3 days prior to surgery. Hemodialysis was also performed on the day of surgery for 193 277 procedures (16.8%). Ninety-day postoperative mortality occurred after 34 944 procedures (3.0%). Longer intervals between the last hemodialysis session and surgery were significantly associated with higher risk of 90-day mortality in a dose-dependent manner (2 days vs 1 day: absolute risk, 4.7% vs 4.2%, absolute risk difference, 0.6% [95% CI, 0.4% to 0.8%], adjusted hazard ratio [HR], 1.14 [95% CI, 1.10 to 1.18]; 3 days vs 1 day: absolute risk, 5.2% vs 4.2%, absolute risk difference, 1.0% [95% CI, 0.8% to 1.2%], adjusted HR, 1.25 [95% CI, 1.19 to 1.31]; and 3 days vs 2 days: absolute risk, 5.2% vs 4.7%, absolute risk difference, 0.4% [95% CI, 0.2% to 0.6%], adjusted HR, 1.09 [95% CI, 1.04 to 1.13]). Undergoing hemodialysis on the same day as surgery was associated with a significantly lower hazard of mortality vs no same-day hemodialysis (absolute risk, 4.0% for same-day hemodialysis vs 4.5% for no same-day hemodialysis; absolute risk difference, -0.5% [95% CI, -0.7% to -0.3%]; adjusted HR, 0.88 [95% CI, 0.84-0.91]). In the analyses that evaluated the interaction between the hemodialysis-to-procedure interval and same-day hemodialysis, undergoing hemodialysis on the day of the procedure significantly attenuated the risk associated with a longer hemodialysis-to-procedure interval (P<.001 for interaction). Conclusions and Relevance: Among Medicare beneficiaries with end-stage kidney disease, longer intervals between hemodialysis and surgery were significantly associated with higher risk of postoperative mortality, mainly among those who did not receive hemodialysis on the day of surgery. However, the magnitude of the absolute risk differences was small, and the findings are susceptible to residual confounding.

Characterizing Equity of Intensive Care Unit Admissions for Sepsis and Acute Respiratory Failure.

Rationale: Patients who identify as from racial or ethnic minority groups who have sepsis or acute respiratory failure (ARF) experience worse outcomes relative to nonminority patients, but processes of care accounting for disparities are not well-characterized. Objectives: Determine whether reductions in intensive care unit (ICU) admission during hospital-wide capacity strain occur preferentially among patients who identify with racial or ethnic minority groups. Methods: This retrospective cohort among 27 hospitals across the Philadelphia metropolitan area and Northern California between 2013 and 2018 included adult patients with sepsis and/or ARF who did not require life support at the time of hospital admission. An updated model of hospital-wide capacity strain was developed that permitted determination of relationships between patient race, ethnicity, ICU admission, and strain. Results: After adjustment for demographics, disease severity, and study hospital, patients who identified as Asian or Pacific Islander had the highest adjusted ICU admission odds relative to patients who identified as White in both the sepsis and ARF populations (odds ratio, 1.09; P = 0.006 and 1.26; P < 0.001). ICU admission was also elevated for patients with ARF who identified as Hispanic (odds ratio, 1.11; P = 0.020). Capacity strain did not modify differences in ICU admission for patients who identified with a minority group in either disease population (all interactions, P > 0.05). Conclusions: Systematic differences in ICU admission patterns were observed for patients that identified as Asian, Pacific Islander, and Hispanic. However, ICU admission was not restricted from these groups, and capacity strain did not preferentially reduce ICU admission from patients identifying with minority groups. Further characterization of provider decision-making can help contextualize these findings as the result of disparate decision-making or a mechanism of equitable care.

Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality.

BACKGROUND: Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone. METHODS: We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation. RESULTS: The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors). CONCLUSIONS: In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.

New complementary perspectives for inpatient physical function assessment: matched clinician-report and patient-report short form measures from the PROMIS adult physical function item bank.

PURPOSE: To develop two item content-matched, precise, score-level targeted inpatient physical function (PF) short form (SF) measures: one clinician-reported, one patient-reported. Items were derived from PROMIS PF bank content; scores are reported on the PROMIS PF T-score metric. METHODS: The PROMIS PF item bank was reviewed for content measuring lower-level PF status (T-scores 10-50) with high item set score-level reliability (≥ 0.90). Selected patient-reported (PR) items were also edited to function as clinician-reported (CR) items. Items were reviewed by clinicians and field tested; responses were assessed for meeting PROMIS measure development standards. New CR and PR items were calibrated using patient responses to the original PROMIS PF items as anchoring data. SFs were constructed, based on content and precision. RESULTS: Nine PROMIS PF items were candidates for CR and PR inpatient PF assessment; three new items were written to extend content coverage. An inpatient sample (N = 515; 55.1% female; mean age = 66.2 years) completed 12 PR items and was assessed by physical therapists (using 12 CR items). Analyses indicated item sets met expected measure development standards. Twelve new CR and three new PR items were linked to the PROMIS PF metric (raw score r = 0.73 and 0.90, respectively). A 5-item CR SF measure was constructed; score-level reliabilities were ≥ 0.90 for T-scores 13-45. A 5-item PR SF measure was assembled, mirroring CR SF content. CONCLUSIONS: Two item content-matched SFs have been developed for clinician and patient reporting and are an effective, efficient means of assessing inpatient PF and offer complementary perspectives.

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives: To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. Methods: We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Measurements and Main Results: Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Conclusions: Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.

Reducing Readmissions for Chronic Obstructive Pulmonary Disease in Response to the Hospital Readmissions Reduction Program.

Rationale: In August 2013, the Hospital Readmission Reduction Program announced financial penalties on hospitals with higher than expected risk-adjusted 30-day readmission rates for Medicare beneficiaries hospitalized for chronic obstructive pulmonary disease (COPD) exacerbation. In October 2014, penalties were imposed. We hypothesized that penalties would be associated with decreased readmissions after COPD hospitalizations. Objectives: To determine whether the announcement and enactment of financial penalties for COPD were associated with decreases in hospital readmissions for COPD. Methods: We used data from California's Office of Statewide Health Planning and Development to examine unplanned 30-day all-cause and COPD-related readmissions after COPD hospitalization. The preannouncement period was January 2010 to July 2013. The postannouncement period was August 2013 to September 2014. The postenactment period was October 2014 to December 2017. Using interrupted time series, we investigated the immediate change after the intervention (level change) and differences in the preintervention and postintervention trends (slope change). Results: We identified 333,429 index hospitalizations for COPD from 449 California hospitals. Overall, 69% of patients had Medicare insurance. For all-cause readmissions, the level change at announcement was 0.16% (95% confidence interval [CI], -1.07 to 1.38; P = 0.80); the change in slope between preannouncement and postannouncement periods was -0.01% (95% CI, -0.15 to 0.13; P = 0.92). The level change at enactment was 0.29% (95% CI, -1.11 to 1.69; P = 0.68); the change in slope between postannouncement and postenactment was 0.04% (95% CI, -0.10 to 0.18; P = 0.57). For patients with COPD-related readmissions, the level change at the time of the announcement was 0.09% (95% CI, -0.68 to 0.85; P = 0.83); the change in slope was 0.003% (95% CI, -0.08 to 0.09; P = 0.94). The level change at the time of the enactment was 0.22% (95% CI, -0.69 to 1.12; P = 0.64); the change in slope was -0.02% (95% CI, -0.10 to 0.07; P = 0.72). Conclusions: We did not detect decreases in either all-cause or COPD-related readmission rates at either time point. Although this would suggest that the Hospital Readmission Reduction Program penalty was ineffective for COPD, COPD readmissions had decreased at an earlier time point (October 2012) when penalties were announced for conditions other than COPD. Based on this, we believe early, broad interventions decreased readmissions, such that no difference was seen at this later time points despite institution of COPD-specific penalties.

Assessment of Value of Neighborhood Socioeconomic Status in Models That Use Electronic Health Record Data to Predict Health Care Use Rates and Mortality.

Importance: Prediction models are widely used in health care as a way of risk stratifying populations for targeted intervention. Most risk stratification has been done using a small number of predictors from insurance claims. However, the utility of diverse nonclinical predictors, such as neighborhood socioeconomic contexts, remains unknown. Objective: To assess the value of using neighborhood socioeconomic predictors in the context of 1-year risk prediction for mortality and 6 different health care use outcomes in a large integrated care system. Design, Setting, and Participants: Diagnostic study using data from all adults age 18 years or older who had Kaiser Foundation Health Plan membership and/or use in the Kaiser Permantente Northern California: a multisite, integrated health care delivery system between January 1, 2013, and June 30, 2014. Data were recorded before the index date for each patient to predict their use and mortality in a 1-year post period using a test-train split for model training and evaluation. Analyses were conducted in fall of 2019. Main Outcomes and Measures: One-year encounter counts (doctor office, virtual, emergency department, elective hospitalizations, and nonelective), total costs, and mortality. Results: A total of 2 951 588 patients met inclusion criteria (mean [SD] age, 47.2 [17.4] years; 47.8% were female). The mean (SD) Neighborhood Deprivation Index was -0.32 (0.84). The areas under the receiver operator curve ranged from 0.71 for emergency department use (using the LASSO method and electronic health record predictors) to 0.94 for mortality (using the random forest method and electronic health record predictors). Neighborhood socioeconomic status predictors did not meaningfully increase the predictive performance of the models for any outcome. Conclusions and Relevance: In this study, neighborhood socioeconomic predictors did not improve risk estimates compared with what is obtainable using standard claims data regardless of model used.

Developing a template matching algorithm for benchmarking hospital performance in a diverse, integrated healthcare system.

Template matching is a proposed approach for hospital benchmarking, which measures performance based on matching a subset of comparable patient hospitalizations from each hospital. We assessed the ability to create the required matched samples and thus the feasibility of template matching to benchmark hospital performance in a diverse healthcare system.Nationwide Veterans Affairs (VA) hospitals, 2017.Observational cohort study.We used administrative and clinical data from 668,592 hospitalizations at 134 VA hospitals in 2017. A standardized template of 300 hospitalizations was selected, and then 300 hospitalizations were matched to the template from each hospital.There was substantial case-mix variation across VA hospitals, which persisted after excluding small hospitals, hospitals with primarily psychiatric admissions, and hospitalizations for rare diagnoses. Median age ranged from 57 to 75 years across hospitals; percent surgical admissions ranged from 0.0% to 21.0%; percent of admissions through the emergency department, 0.1% to 98.7%; and percent Hispanic patients, 0.2% to 93.3%. Characteristics for which there was substantial variation across hospitals could not be balanced with any matching algorithm tested. Although most other variables could be balanced, we were unable to identify a matching algorithm that balanced more than ∼20 variables simultaneously.We were unable to identify a template matching approach that could balance hospitals on all measured characteristics potentially important to benchmarking. Given the magnitude of case-mix variation across VA hospitals, a single template is likely not feasible for general hospital benchmarking.

Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.

Importance: The electronic health record (EHR) is a source of practitioner dissatisfaction in part because of challenges with information retrieval. To improve data accessibility, a better understanding of practitioners' information needs within individual patient records is needed. Objective: To assess EHR users' searches using data from a large integrated health care system. Design, Setting, and Participants: This retrospective cross-sectional analysis used EHR search data from Kaiser Permanente Northern California, an integrated health care delivery system with more than 4.4 million members. Users' EHR search activity data were obtained from April 1, 2018, to May 15, 2019. Main Outcomes and Measures: Search term frequency was grouped by user and practitioner types. Network analyses were performed of co-occurring search terms within a single search episode, and centrality measures for search terms (degree and betweenness centrality) were calculated. Results: A total of 12 313 047 search activities (including 4 328 330 searches and 7 984 717 result views) conducted by 34 735 unique users within 977 160 unique patient EHRs were identified. In aggregate, users searched for 208 374 unique search terms and conducted a median of 4 searches (interquartile range, 1-28 searches). Of all 97 367 active EHR users, 34 735 (35.7%) conducted at least 1 search. However, of all 12 968 active EHR physician users, 9801 (75.6%) conducted at least 1 search, and of all 1908 active pharmacist users, 1402 (73.5%) conducted at least 1 search. The top 3 most commonly searched terms were statin (75 017 searches [1.7%]), colonoscopy (73 545 [1.7%]), and pft (54 990 [1.3%]). However, wide variation in top searches were noted across practitioner groups. Terms searched most often with another term in a single linked search episode included statin, lisinopril, colonoscopy, gabapentin, and aspirin. Conclusions and Relevance: Although physicians and pharmacists were the most active users of EHR searches, search volume and frequently searched terms varied considerably by and within user role. Further customization of the EHR interface may help leverage users' search content and patterns to improve targeted information retrieval.

Multiyear Rehospitalization Rates and Hospital Outcomes in an Integrated Health Care System.

Importance: Since the introduction of the rehospitalization rate as a quality measure, multiple changes have taken place in the US health care delivery system. Interpreting rehospitalization rates without taking a global view of these changes and new data elements from comprehensive electronic medical records yields a limited assessment of the quality of care. Objective: To examine hospitalization outcomes from a broad perspective, including the implications of numerator and denominator definitions, all adult patients with all diagnoses, and detailed clinical data. Design, Setting, and Participants: This cohort study obtained data from 21 hospitals in Kaiser Permanente Northern California (KPNC), an integrated health care delivery system that serves patients with Medicare Advantage plans, Medicaid, and/or Kaiser Foundation Health Plan. The KPNC electronic medical record system was used to capture hospitalization data for adult patients who were 18 years of age or older; discharged from June 1, 2010, through December 31, 2017; and hospitalized for reasons other than childbirth. Hospital stays for transferred patients were linked using public and internal sources. Exposures: Hospitalization type (inpatient, for observation only), comorbidity burden, acute physiology score, and care directives. Main Outcomes and Measures: Mortality (inpatient, 30-day, and 30-day postdischarge), nonelective rehospitalization, and discharge disposition (home, home with home health assistance, regular skilled nursing facility, or custodial skilled nursing facility). Results: In total, 1 384 025 hospitalizations were identified, of which 1 155 034 (83.5%) were inpatient and 228 991 (16.5%) were for observation only. These hospitalizations involved 679 831 patients (mean [SD] age, 61.4 [18.1] years; 362 582 female [53.3%]). The number of for-observation-only hospitalizations increased from 16 497 (9.4%) in the first year of the study to 120 215 (20.5%) in the last period of the study, whereas inpatient hospitalizations with length of stay less than 24 hours decreased by 33% (from 12 008 [6.9%] to 27 108 [4.6%]). Illness burden measured using administrative data or acute physiology score increased significantly. The proportion of patients with a Comorbidity Point Score of 65 or higher increased from 20.5% (range across hospitals, 18.4%-26.4%) to 28.8% (range, 22.3%-33.0%), as did the proportion with a Charlson Comorbidity Index score of 4 or higher, which increased from 28.8% (range, 24.6%-35.0%) to 38.4% (range, 31.9%-43.4%). The proportion of patients at or near critical illness (Laboratory-based Acute Physiology Score [LAPS2] ≥110) increased by 21.4% (10.3% [range across hospitals, 7.4%-14.7%] to 12.5% [range across hospitals, 8.3%-16.6%]; P < .001), reflecting a steady increase of 0.07 (95% CI, 0.04-0.10) LAPS2 points per month. Unadjusted inpatient mortality in the first year of the study was 2.78% and in the last year was 2.71%; the corresponding numbers for 30-day mortality were 5.88% and 6.15%, for 30-day postdischarge mortality were 3.94% and 4.22%, and for nonelective rehospitalization were 12.00% and 12.81%, respectively. All outcomes improved after risk adjustment. Compared with the first month, the final observed to expected ratio was 0.79 (95% CI, 0.73-0.84) for inpatient mortality, 0.86 (95% CI, 0.82-0.89) for 30-day mortality, 0.90 (95% CI, 0.85-0.95) for 30-day nonelective rehospitalization, and 0.87 (95% CI, 0.83-0.92) for 30-day postdischarge mortality. The proportion of nonelective rehospitalizations meeting public reporting criteria decreased substantially over the study period (from 58.0% in 2010-2011 to 45.2% in 2017); most of this decrease was associated with the exclusion of observation stays. Conclusions and Relevance: This study found that in this integrated system, the hospitalization rate decreased and risk-adjusted hospital outcomes improved steadily over the 7.5-year study period despite worsening case mix. The comprehensive results suggest that future assessments of care quality should consider the implications of numerator and denominator definitions, display multiple metrics concurrently, and include all hospitalization types and detailed data.

The number needed to benefit: estimating the value of predictive analytics in healthcare.

Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that together are expected to impact model benefit. These include factors relevant to model prediction (including the number needed to screen) as well as those relevant to the subsequent action (number needed to treat). In the simplest terms, a number needed to benefit contextualizes the numbers needed to screen and treat, offering an opportunity to estimate the value of a clinical predictive model in action.

The Impact of Pharmacy-specific Predictors on the Performance of 30-Day Readmission Risk Prediction Models.

RESEARCH OBJECTIVE: Pharmacists are an expensive and limited resource in the hospital and outpatient setting. A pharmacist can spend up to 25% of their day planning. Time spent planning is time not spent delivering an intervention. A readmission risk adjustment model has potential to be used as a universal outcome-based prioritization tool to help pharmacists plan their interventions more efficiently. Pharmacy-specific predictors have not been used in the constructs of current readmission risk models. We assessed the impact of adding pharmacy-specific predictors on performance of readmission risk prediction models. STUDY DESIGN: We used an observational retrospective cohort study design to assess whether pharmacy-specific predictors such as an aggregate pharmacy score and drug classes would improve the prediction of 30-day readmission. A model of age, sex, length of stay, and admission category predictors was used as the reference model. We added predictor variables in sequential models to evaluate the incremental effect of additional predictors on the performance of the reference. We used logistic regression to regress the outcomes on predictors in our derivation dataset. We derived and internally validated our models through a 50:50 split validation of our dataset. POPULATION STUDIED: Our study population (n=350,810) was of adult admissions at hospitals in a large integrated health care delivery system. PRINCIPAL FINDINGS: Individually, the aggregate pharmacy score and drug classes caused a nearly identical but moderate increase in model performance over the reference. As a single predictor, the comorbidity burden score caused the greatest increase in model performance when added to the reference. Adding the severity of illness score, comorbidity burden score and the aggregate pharmacy score to the reference caused a cumulative increase in model performance with good discrimination (c statistic, 0.712; Nagelkerke R, 0.112). The best performing model included all predictors: severity of illness score, comorbidity burden score, aggregate pharmacy score, diagnosis groupings, and drug subgroups. CONCLUSIONS: Adding the aggregate pharmacy score to the reference model significantly increased the c statistic but was out-performed by the comorbidity burden score model in predicting readmission. The need for a universal prioritization tool for pharmacists may therefore be potentially met with the comorbidity burden score model. However, the aggregate pharmacy score and drug class models still out-performed current Medicare readmission risk adjustment models. IMPLICATIONS FOR POLICY OR PRACTICE: Pharmacists have a great role in preventing readmission, and therefore can potentially use one of our models: comorbidity burden score model, aggregate pharmacy score model, drug class model or complex model (a combination of all 5 major predictors) to prioritize their interventions while exceeding Medicare performance measures on readmission. The choice of model to use should be based on the availability of these predictors in the health care system.

Comparing Hospital Processes and Outcomes in California Medicare Beneficiaries: Simulation Prompts Reconsideration.

INTRODUCTION: This article is not a traditional research report. It describes how conducting a specific set of benchmarking analyses led us to broader reflections on hospital benchmarking. We reexamined an issue that has received far less attention from researchers than in the past: How variations in the hospital admission threshold might affect hospital rankings. Considering this threshold made us reconsider what benchmarking is and what future benchmarking studies might be like. Although we recognize that some of our assertions are speculative, they are based on our reading of the literature and previous and ongoing data analyses being conducted in our research unit. We describe the benchmarking analyses that led to these reflections. OBJECTIVES: The Centers for Medicare and Medicaid Services' Hospital Compare Web site includes data on fee-for-service Medicare beneficiaries but does not control for severity of illness, which requires physiologic data now available in most electronic medical records.To address this limitation, we compared hospital processes and outcomes among Kaiser Permanente Northern California's (KPNC) Medicare Advantage beneficiaries and non-KPNC California Medicare beneficiaries between 2009 and 2010. METHODS: We assigned a simulated severity of illness measure to each record and explored the effect of having the additional information on outcomes. RESULTS: We found that if the admission severity of illness in non-KPNC hospitals increased, KPNC hospitals' mortality performance would appear worse; conversely, if admission severity at non-KPNC hospitals' decreased, KPNC hospitals' performance would appear better. CONCLUSION: Future hospital benchmarking should consider the impact of variation in admission thresholds.