Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare data age. This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be better together

A Real-Time Screening Alert Improves Patient Recruitment Efficiency

The scarcity of cost-effective patient identification methods represents a significant barrier to clinical research. Research recruitment alerts have been designed to facilitate physician referrals but limited support is available to clinical researchers. We conducted a retrospective data analysis to evaluate the efficacy of a real-time patient identification alert delivered to clinical research coordinators recruiting for a clinical prospective cohort study. Data from log analysis and informal interviews with coordinators were triangulated. Over a 12-month period, 11,295 were screened electronically, 1,449 were interviewed, and 282 were enrolled. The enrollment rates for the alert and two other conventional methods were 4.65%, 2.01%, and 1.34% respectively. A taxonomy of eligibility status was proposed to precisely categorize research patients. Practical ineligibility factors were identified and their correlation with age and gender were analyzed. We conclude that the automatic prescreening alert improves screening efficiency and is an effective aid to clinical research coordinators

Quality of Life in Chronic Pancreatitis is Determined by Constant Pain, Disability/Unemployment, Current Smoking, and Associated Co-Morbidities

OBJECTIVES: Chronic pancreatitis (CP) has a profound independent effect on quality of life (QOL). Our aim was to identify factors that impact the QOL in CP patients. METHODS: We used data on 1,024 CP patients enrolled in the three NAPS2 studies. Information on demographics, risk factors, co-morbidities, disease phenotype, and treatments was obtained from responses to structured questionnaires. Physical and mental component summary (PCS and MCS, respectively) scores generated using responses to the Short Form-12 (SF-12) survey were used to assess QOL at enrollment. Multivariable linear regression models determined independent predictors of QOL. RESULTS: Mean PCS and MCS scores were 36.7+/-11.7 and 42.4+/-12.2, respectively. Significant (P \u3c 0.05) negative impact on PCS scores in multivariable analyses was noted owing to constant mild-moderate pain with episodes of severe pain or constant severe pain (10 points), constant mild-moderate pain (5.2), pain-related disability/unemployment (5.1), current smoking (2.9 points), and medical co-morbidities. Significant (P \u3c 0.05) negative impact on MCS scores was related to constant pain irrespective of severity (6.8-6.9 points), current smoking (3.9 points), and pain-related disability/unemployment (2.4 points). In women, disability/unemployment resulted in an additional 3.7 point reduction in MCS score. Final multivariable models explained 27% and 18% of the variance in PCS and MCS scores, respectively. Etiology, disease duration, pancreatic morphology, diabetes, exocrine insufficiency, and prior endotherapy/pancreatic surgery had no significant independent effect on QOL. CONCLUSIONS: Constant pain, pain-related disability/unemployment, current smoking, and concurrent co-morbidities significantly affect the QOL in CP. Further research is needed to identify factors impacting QOL not explained by our analyses

Real World Performance of the 21st Century Cures Act Population Level Application Programming Interface

OBJECTIVE: To evaluate the real-world performance in delivering patient data on populations, of the SMART/HL7 Bulk FHIR Access API, required in Electronic Health Records (EHRs) under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at five healthcare sites from April to September 2023, including four hospitals using EHRs certified for interoperability, and one Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across six types of FHIR resources. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8,000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1,555-2,500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12,000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes

Measurement of the WW Boson Mass

A measurement of the mass of the $W$ boson is presented based on a sample of 5982 $W \rightarrow e \nu$ decays observed in $p\overline{p}$ collisions at $\sqrt{s}$ = 1.8~TeV with the D\O\ detector during the 1992--1993 run. From a fit to the transverse mass spectrum, combined with measurements of the $Z$ boson mass, the $W$ boson mass is measured to be $M_W = 80.350 \pm 0.140 (stat.) \pm 0.165 (syst.) \pm 0.160 (scale) GeV/c^2$.Comment: 12 pages, LaTex, style Revtex, including 3 postscript figures (submitted to PRL

Second Generation Leptoquark Search in p\bar{p} Collisions at s\sqrt{s} = 1.8 TeV

We report on a search for second generation leptoquarks with the D\O\ detector at the Fermilab Tevatron $p\bar{p}$ collider at $\sqrt{s}$ = 1.8 TeV. This search is based on 12.7 pb$^{-1}$ of data. Second generation leptoquarks are assumed to be produced in pairs and to decay into a muon and quark with branching ratio $\beta$ or to neutrino and quark with branching ratio $(1-\beta)$. We obtain cross section times branching ratio limits as a function of leptoquark mass and set a lower limit on the leptoquark mass of 111 GeV/c$^{2}$ for $\beta = 1$ and 89 GeV/c$^{2}$ for $\beta = 0.5$ at the 95%\ confidence level.Comment: 18 pages, FERMILAB-PUB-95/185-

Search for W~1Z~2\widetilde{W}_1\widetilde{Z}_2 Production via Trilepton Final States in ppˉp\bar{p} collisions at s=1.8\sqrt{s}=1.8 TeV

We have searched for associated production of the lightest chargino, $\widetilde{W}_1$, and next-to-lightest neutralino, $\widetilde{Z}_2$, of the Minimal Supersymmetric Standard Model in $p\bar{p}$ collisions at \mbox{$\sqrt{s}$ = 1.8 TeV} using the \D0 detector at the Fermilab Tevatron collider. Data corresponding to an integrated luminosity of 12.5$\pm 0.7$ \ipb were examined for events containing three isolated leptons. No evidence for $\widetilde{W}_1\widetilde{Z}_2$ pair production was found. Limits on $\sigma(\widetilde{W}_1\widetilde{Z}_2)$Br$(\widetilde{W}_1\to l\nu\widetilde{Z}_1)$Br$(\widetilde{Z}_2\to l\bar{l}\widetilde{Z}_1)$ are presented.Comment: 17 pages (13 + 1 page table + 3 pages figures). 3 PostScript figures will follow in a UUEncoded, gzip'd, tar file. Text in LaTex format. Submitted to Physical Review Letters. Replace comments - Had to resumbmit version with EPSF directive

The Azimuthal Decorrelation of Jets Widely Separated in Rapidity

This study reports the first measurement of the azimuthal decorrelation between jets with pseudorapidity separation up to five units. The data were accumulated using the D{\O}detector during the 1992--1993 collider run of the Fermilab Tevatron at $\sqrt{s}=$ 1.8 TeV. These results are compared to next--to--leading order (NLO) QCD predictions and to two leading--log approximations (LLA) where the leading--log terms are resummed to all orders in $\alpha_{\scriptscriptstyle S}$. The final state jets as predicted by NLO QCD show less azimuthal decorrelation than the data. The parton showering LLA Monte Carlo {\small HERWIG} describes the data well; an analytical LLA prediction based on BFKL resummation shows more decorrelation than the data.Comment: 6 pages with 4 figures, all uuencoded and gzippe

Use of Electronic Health Records to Support a Public Health Response to the COVID-19 Pandemic in the United States: A Perspective from Fifteen Academic Medical Centers

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencie