Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort.

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

A Tent-Inspired Portable Solar-Driven Water Purification Device for Wilderness Explorers.

Wilderness adventure favored by many enthusiasts often endanger lives due to lacking freshwater or drinking contaminated water. Therefore, compared to the inefficient methods of filtration, steaming, and direct solar heating, it is of great meaningfulness to develop a solar-driven water purification device with efficiency, lightweight, portability, and multi-water-quality purification by taking full advantage of solar-driven interfacial evaporation. Here, a tent-inspired portable solar-driven water purification device consisting of Janus-structured bacterial cellulose aerogel (JBCA) solar evaporator and tent-type condensation recovery device is reported. For the JBCA solar evaporator, it is prepared from biomass bacterial cellulose (BC) as raw material and hydroxylated carbon nanotubes (HCNT) as photothermal material, and the Janus property is achieved by the assistance of hydrophobic and hydrophilic chemical cross-linking. It exhibits lightweight, unibody, high photothermal conversion, efficient evaporation, and multi-water-quality purification capability for representative seawater, urine, and bacterial river water. For the tent-type condensation recovery device, it is based on the prototype of tent and uses flexible ultra-transparent polyvinyl chloride (PVC) film as raw material. Thanks to the rational prototype and material selection, it displays outstanding portability and lightweight through the folding/unfolding method. Therefore, the designed tent-inspired portable solar-driven water purification device demonstrates great potential application in wilderness exploration.

Portability of IMRT QA between matched linear accelerators.

PURPOSE: To determine if patient-specific IMRT quality assurance can be measured on any matched treatment delivery system (TDS) for patient treatment delivery on another. METHODS: Three VMAT plans of varying complexity were created for each available energy for head and neck, SBRT lung, and right chestwall anatomical sites. Each plan was delivered on three matched Varian TrueBeam TDSs to the same Scandidos Delta4 Phantom+ diode array with only energy-specific device calibrations. Dose distributions were corrected for TDS output and then compared to TPS calculations using gamma analysis. Round-robin comparisons between measurements from each TDS were also performed using point-by-point dose difference, median dose difference, and the percent of point dose differences within 2% of the mean metrics. RESULTS: All plans had more than 95% of points passing a gamma analysis using 3%/3 mm criteria with global normalization and a 20% threshold when comparing measurements to calculations. The tightest gamma analysis criteria where a plan still passed > 95% were similar across delivery systems-within 0.5%/0.5 mm for all but three plan/energy combinations. Median dose deviations in measurement-to-measurement comparisons were within 0.7% and 1.0% for global and local normalization, respectively. More than 90% of the point differences were within 2%. CONCLUSION: A set of plans spanning available energies and complexity levels were delivered by three matched TDSs. Comparisons to calculations and between measurements showed dose distributions delivered by each TDS using the same DICOM RT-plan file meet tolerances much smaller than typical clinical IMRT QA criteria. This demonstrates each TDS is modeled to a similar accuracy by a common class (shared) beam model. Additionally, it demonstrates that dose distributions from one TDS show small differences in median dose to the others. This is an important validation component of the common beam model approach, allowing for operational improvements in the clinic.

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

Portable and integrated microfluidic flow control system using off-the-shelf components towards organs-on-chip applications.

Organ-on-a-chip (OoC) devices require the precise control of various media. This is mostly done using several fluid control components, which are much larger than the typical OoC device and connected through fluidic tubing, i.e., the fluidic system is not integrated, which inhibits the system's portability. Here, we explore the limits of fluidic system integration using off-the-shelf fluidic control components. A flow control configuration is proposed that uses a vacuum to generate a fluctuation-free flow and minimizes the number of components used in the system. 3D printing is used to fabricate a custom-designed platform box for mounting the chosen smallest footprint components. It provides flexibility in arranging the various components to create experiment-specific systems. A demonstrator system is realized for lung-on-a-chip experiments. The 3D-printed platform box is 290 mm long, 240 mm wide and 37 mm tall. After integrating all the components, it weighs 4.8 kg. The system comprises of a switch valve, flow and pressure controllers, and a vacuum pump to control the diverse media flows. The system generates liquid flow rates ranging from 1.5 [Formula: see text]Lmin[Formula: see text] to 68 [Formula: see text]Lmin[Formula: see text] in the cell chambers, and a cyclic vacuum of 280 mbar below atmospheric pressure with 0.5 Hz frequency in the side channels to induce mechanical strain on the cells-substrate. The components are modular for easy exchange. The battery operated platform box can be mounted on either upright or inverted microscopes and fits in a standard incubator. Overall, it is shown that a compact integrated and portable fluidic system for OoC experiments can be constructed using off-the-shelf components. For further down-scaling, the fluidic control components, like the pump, switch valves, and flow controllers, require significant miniaturization while having a wide flow rate range with high resolution.

Progressive Learning of a Multimodal Classifier Accounting for Different Modality Combinations.

In classification tasks, such as face recognition and emotion recognition, multimodal information is used for accurate classification. Once a multimodal classification model is trained with a set of modalities, it estimates the class label by using the entire modality set. A trained classifier is typically not formulated to perform classification for various subsets of modalities. Thus, the model would be useful and portable if it could be used for any subset of modalities. We refer to this problem as the multimodal portability problem. Moreover, in the multimodal model, classification accuracy is reduced when one or more modalities are missing. We term this problem the missing modality problem. This article proposes a novel deep learning model, termed KModNet, and a novel learning strategy, termed progressive learning, to simultaneously address missing modality and multimodal portability problems. KModNet, formulated with the transformer, contains multiple branches corresponding to different k-combinations of the modality set S. KModNet is trained using a multi-step progressive learning framework, where the k-th step uses a k-modal model to train different branches up to the k-th combination branch. To address the missing modality problem, the training multimodal data is randomly ablated. The proposed learning framework is formulated and validated using two multimodal classification problems: audio-video-thermal person classification and audio-video emotion classification. The two classification problems are validated using the Speaking Faces, RAVDESS, and SAVEE datasets. The results demonstrate that the progressive learning framework enhances the robustness of multimodal classification, even under the conditions of missing modalities, while being portable to different modality subsets.

An Aluminum-Based Microfluidic Chip for Polymerase Chain Reaction Diagnosis.

Real-time polymerase chain reaction (real-time PCR) tests were successfully conducted in an aluminum-based microfluidic chip developed in this work. The reaction chamber was coated with silicone-modified epoxy resin to isolate the reaction system from metal surfaces, preventing the metal ions from interfering with the reaction process. The patterned aluminum substrate was bonded with a hydroxylated glass mask using silicone sealant at room temperature. The effect of thermal expansion was counteracted by the elasticity of cured silicone. With the heating process closely monitored, real-time PCR testing in reaction chambers proceeded smoothly, and the results show similar quantification cycle values to those of traditional test sets. Scanning electron microscope (SEM) and atomic force microscopy (AFM) images showed that the surface of the reaction chamber was smoothly coated, illustrating the promising coating and isolating properties. Energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma-optical emission spectrometer (ICP-OES) showed that no metal ions escaped from the metal to the chip surface. Fourier-transform infrared spectroscopy (FTIR) was used to check the surface chemical state before and after tests, and the unchanged infrared absorption peaks indicated the unreacted, antifouling surface. The limit of detection (LOD) of at least two copies can be obtained in this chip.

The Patient Role in a Federal National-Scale Health Information Exchange.

The federal Trusted Exchange Framework and Common Agreement (TEFCA) aims to reduce fragmentation of patient records by expanding query-based health information exchange with nationwide connectivity for diverse purposes. TEFCA provides a common agreement and security framework allowing clinicians, and possibly insurance company staff, public health officials, and other authorized users, to query for health information about hundreds of millions of patients. TEFCA presents an opportunity to weave information exchange into the fabric of our national health information economy. We define 3 principles to promote patient autonomy and control within TEFCA: (1) patients can query for data about themselves, (2) patients can know when their data are queried and shared, and (3) patients can configure what is shared about them. We believe TEFCA should address these principles by the time it launches. While health information exchange already occurs on a large scale today, the launch of TEFCA introduces a major, new, and cohesive component of 21st-century US health care information infrastructure. We strongly advocate for a substantive role for the patient in TEFCA, one that will be a model for other systems and policies.

The ATI-ET Triangle Model: A Novel Approach to Estimate Soil Moisture Applied to MODIS Data.

A simple soil moisture (SM) estimation method is proposed using apparent thermal inertia (ATI) and evapotranspiration (ET) data. Among the methods of estimating SM by using thermal infrared (TIR) remote sensing, the ATI method is widely used in bare soil and low vegetation areas. However, large surface ET will cause ATI error, resulting in lower accuracy of SM estimation. To overcome this problem, the potential of ATI-ET space for estimating the SM of bare and vegetated farmland in the dry season (no irrigation) is studied. ATI and ET data were used to construct triangle feature space, and six distance parameters are extracted from the positions of random pixels in the triangle. Some correlation estimates were made to derive those parameters that were useful for SM estimation, which were three in total. The SM estimation model consisting of these three parameters was built. Compared with the ATI model, the ATI-ET triangle model can not only be applied to areas with high ET, but also has higher accuracy in estimating SM. The ATI-ET triangle model is more suitable for application in bare soil and low vegetation areas. As the Normalized Difference Vegetation Index increases, the accuracy of the model estimates decreases. To show the high portability of the proposed model for SM estimation, we chose another set of in situ SM data acquired in Tibetan Plateau. The results proved the effectiveness of the model in other similar study regions.

The Impact of Patient Access to Their Electronic Health Record on Medication Management Safety: A Narrative Review.

Introduction: As the American's Federal Health Insurance Portability and Accountability Act (HIPAA) stated that patients should be allowed to review their medical records, and as information technology is ever more widely used by healthcare professionals and patients, providing patients with online access to their own medical records through a patient portal is becoming increasingly popular. Previous research has been done regarding the impact on the quality and safety of patients' care, rather than explicitly on medication safety, when providing those patients with access to their electronic health records (EHRs). Aim: This narrative review aims to summarise the results from previous studies on the impact on medication management safety concepts of adult patients accessing information contained in their own EHRs. Result: A total of 24 studies were included in this review. The most two commonly studied measures of safety in medication management were: (a) medication adherence and (b) patient-reported experience. Other measures, such as: discrepancies, medication errors, appropriateness and Adverse Drug Events (ADEs) were the least studied. Conclusion: The results suggest that providing patients with access to their EHRs can improve medication management safety. Patients pointed out improvements to the safety of their medications and perceived stronger medication control. The data from these studies lay the foundation for future research.

Half a century later and we're back where we started: How the problem of locality turned in to the problem of portability.

In the 1970s, Lewontin sparked a debate about a problem of locality, by making the case that any given heritability estimate is local to the original population and environment studied, and could not be generalized to other populations and environments. Nearly 50 years later, a new problem of portability has emerged: the predictive accuracy of polygenic scores diminishes when applied to populations whose characteristics are different from the original population sample. This paper briefly reviews the nature of each problem and analyzes their similarities and differences in three areas: 1) conceptual underpinnings, 2) causal explanations, and 3) practical, social, and political implications. Although conceptually and methodologically different from the problem of locality in important respects, the problem of portability facing contemporary genomics today should come as no surprise, as it is an inevitable outcome of the kinds of problematic inferences detailed by Lewontin nearly half a century ago.

Collective knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces.

This article provides the motivation and overview of the Collective Knowledge Framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable components that encapsulate research artifacts and provide unified application programming interfaces (APIs), command-line interfaces (CLIs), meta descriptions and common automation actions for related artifacts. The CK framework is used to organize and manage research projects as a database of such components. Inspired by the USB 'plug and play' approach for hardware, CK also helps to assemble portable workflows that can automatically plug in compatible components from different users and vendors (models, datasets, frameworks, compilers, tools). Such workflows can build and run algorithms on different platforms and environments in a unified way using the customizable CK program pipeline with software detection plugins and the automatic installation of missing packages. This article presents a number of industrial projects in which the modular CK approach was successfully validated in order to automate benchmarking, auto-tuning and co-design of efficient software and hardware for machine learning and artificial intelligence in terms of speed, accuracy, energy, size and various costs. The CK framework also helped to automate the artifact evaluation process at several computer science conferences as well as to make it easier to reproduce, compare and reuse research techniques from published papers, deploy them in production, and automatically adapt them to continuously changing datasets, models and systems. The long-term goal is to accelerate innovation by connecting researchers and practitioners to share and reuse all their knowledge, best practices, artifacts, workflows and experimental results in a common, portable and reproducible format at https://cKnowledge.io/. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

The health sphere beyond borders: Coverage portability and justice in a global space.

Medical coverage often stops at borders, for both travellers and long-term migrants. Such patchiness imposes a de facto limit on free movement. This article considers this phenomenon not as a mere policy choice or technical matter, but as a form of territorial discrimination that is incoherent and even unjust. This legacy of nationally bounded social citizenship rests on a mistaken version of solidarity. Moreover, with growing mobility and rising expectations of medical coverage around the world, the fragmenting of safety nets by the political honeycomb of statehood will become a vexing problem in coming decades. A proper understanding of the health sphere not only can justify incremental reforms that lessen territorial discrimination, without impairing either solidarity or sustainability. It also foreshadows a radically different vision of how social provision might work in a future global space beyond the nation-state.

Interinstitutional beam model portability study in a mixed vendor environment.

A 6 MV flattened beam model for a Varian TrueBeamSTx c-arm treatment delivery system in RayStation, developed and validated at one institution, was implemented and validated at another institution. The only parameter value adjustments were to accommodate machine output at the second institution. Validation followed MPPG 5.a. recommendations, with particular attention paid to IMRT and VMAT deliveries. With this minimal adjustment, the model passed validation across a broad spectrum of treatment plans, measurement devices, and staff who created the test plans and executed the measurements. This work demonstrates the possibility of using a single template model in the same treatment planning system with matched machines in a mixed vendor environment.

Concurrent Validity and Reliability of a Handheld Dynamometer in Measuring Isometric Shoulder Rotational Strength.

CONTEXT: Measuring isometric shoulder rotational strength is clinically important for evaluating motor disability in athletes with shoulder injuries. Recent evidence suggests that handheld dynamometry may provide a low-cost and portable method for the clinical assessment of isometric shoulder strength. OBJECTIVE: To investigate the concurrent validity and the intrarater and interrater reliability of handheld dynamometry for measuring isometric shoulder rotational strength. DESIGN: Cross-sectional study. SETTING: Biomechanics laboratory. PARTICIPANTS: Thirty-nine young, healthy participants. MAIN OUTCOME MEASURES: The peak isometric strength of the internal rotators and external rotators, measured by handheld dynamometry (in newton) and isokinetic dynamometry (in newton meter). INTERVENTIONS: Maximal isometric shoulder rotational strength was measured as participants lay supine with 90° shoulder abduction, neutral rotation, 90° elbow flexion, and forearm pronation. Measurements were performed independently by 2 different physiotherapists and in 3 different sessions to evaluate interrater and intrarater reliability. The data obtained by handheld dynamometry were compared with those obtained by isokinetic testing to evaluate concurrent validity. RESULTS: The intraclass correlation coefficients for interrater reliability in measuring maximum isometric shoulder external and internal rotation strength were .914 (95% confidence interval [CI], .842-.954) and .842 (95% CI, .720-.914), respectively. The intrarater reliability values of the method for measuring maximal shoulder external and internal rotation strength were 0.865 (95% CI, 0.757-0.927) and 0.901 (95% CI, 0.820-0.947), respectively. The Pearson correlation coefficients between the handheld and isokinetic dynamometer measurements were .792 (95% CI, .575-.905) for external rotation strength and .664 (95% CI, .419-.839) for internal rotation strength. CONCLUSIONS: The handheld dynamometer showed good to excellent reliability and moderate to good validity in measuring maximum isometric shoulder rotational strength. Therefore, handheld dynamometry could be acceptable for health and sports professionals in field situations to evaluate maximum isometric shoulder rotational strength.

Rapid Detection Methods for Bacterial Pathogens in Ambient Waters at the Point of Sample Collection: A Brief Review.

The world is currently facing a serious health burden of waterborne diseases, including diarrhea, gastrointestinal diseases, and systemic illnesses. The control of these infectious diseases ultimately depends on the access to safe drinking water, properly managed sanitation, and hygiene practices. Therefore, ultrasensitive, rapid, and specific monitoring platforms for bacterial pathogens in ambient waters at the point of sample collection are urgently needed. We conducted a literature review on state-of-the-art research of rapid in-field aquatic bacteria detection methods, including cell-based methods, nucleic acid amplification detection methods, and biosensors. The detection performance, the advantages, and the disadvantages of the technologies are critically discussed. We envision that promising monitoring approaches should be automated, real-time, and target-multiplexed, thus allowing comprehensive evaluation of exposure risks attributable to waterborne pathogens and even emerging microbial contaminants such as antibiotic resistance genes, which leads to better protection of public health.

Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping.

Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9-48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.

Doctors Routinely Share Health Data Electronically Under HIPAA, and Sharing With Patients and Patients' Third-Party Health Apps is Consistent: Interoperability and Privacy Analysis.

Since 2000, federal regulations have affirmed that patients have a right to a complete copy of their health records from their physicians and hospitals. Today, providers across the nation use electronic health records and electronic information exchange for health care, and patients are choosing digital health apps to help them manage their own health and health information. Some doctors and health systems have voiced concern about whether they may transmit a patient's data upon the patient's request to the patient or the patient's health app. This hesitation impedes shared information and care coordination with patients. It impairs patients' ability to use the state-of-the-art digital health tools they choose to track and manage their health. It undermines the ability of patients' family caregivers to monitor health and to work remotely to provide care by using the nearly unique capabilities of health apps on people's smartphones. This paper explains that sharing data electronically with patients and patients' third-party apps is legally consistent under the Health Insurance Portability and Accountability Act (HIPAA) with routine electronic data sharing with other doctors for treatment or with insurers for reimbursement. The paper explains and illustrates basic principles and scenarios around sharing with patients, including patients' third-party apps. Doctors routinely and legally share health data electronically under HIPAA whether or not their organizations retain HIPAA responsibility. Sharing with patients and patients' third-party apps is no different and should be just as routine.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

BACKGROUND: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. METHODS: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. RESULTS: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ±â€¯1.38. Specifically, the average knowledge (K) score is 0.64 ±â€¯0.66, interpretation (I) score is 0.33 ±â€¯0.55, and programming (P) score is 0.40 ±â€¯0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. CONCLUSION: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.