The NQRN Registry Maturational Framework: Evaluating the Capability and Use of Clinical Registries.

Clinical registries are increasingly used as national performance measurement platforms. In 2018, nearly 70 percent of the more than 50 specialty society registries in the United States were used by the Centers for Medicare & Medicaid Services (CMS) to measure the quality of clinical care. Private payers and evaluating organizations also use or desire to use registry information to inform quality improvement programs and value-based payment models. The requirements for an entity to become a CMS Qualified Clinical Data Registry (QCDR) constitute a minimum set of standards for the purpose of reporting to the CMS Quality Payment Program. Models and frameworks exist that can help classify registries by purpose and use, and maturity models are available for evaluating health IT systems generally. However, there is currently no framework that describes the capability that should be expected from a registry at different phases of its development and maturity. In response, the National Quality Registry Network (NQRN) has developed a registry maturational framework. The framework models early, intermediate and mature development phases, the capabilities anticipated during these phases and 17 domains across which registry programs support those capabilities. The framework was developed and refined by NQRN registry stewards, users and other stakeholders between 2013-2018. It is intended to be used as a developmental guide or for registry evaluation. The successful use of registry information to execute value-based payment models is a critical need in U.S. health care. The NQRN framework can help ensure that our national system of registries is rising to the occasion.

Improving Interoperability between Registries and EHRs.

National performance measurement needs clinical data that track the performance of multi disciplinary teams across episodes of care. Clinical registries are ideal platforms for this work due to their capture of structured, specific data across specialties. Because registries collect data at a national level, and registry data are captured in a consistent structure and format within each registry, registry data are useful for measurement and analysis "out of the box". Registry business models are hampered by the cost of collecting data from EHRs and other source systems and abstracting or mapping them to fit registry data models. The National Quality Registry Network (NQRN) has launched Registries on FHIR, an initiative to lower barriers to achieving semantic interoperability between registries and source data systems. In 2017 Registries on FHIR conducted an information gathering campaign to learn where registries want better interoperability, and how to go about improving it.

The Use of Clinical Registries in the United States: A Landscape Survey.

INTRODUCTION: The use of information from clinical registries for improvement and value-based payment is increasing, yet information about registry use is not widely available. We conducted a landscape survey to understand registry uses, focus areas and challenges. The survey addressed the structure and organization of registry programs, as well as their purpose and scope. SETTING: The survey was conducted by the National Quality Registry Network (NQRN), a community of organizations interested in registries. NQRN is a program of the PCPI, a national convener of medical specialty and professional societies and associations, which constitute a majority of registry stewards in the United States. METHODS: We surveyed 152 societies and associations, asking about registry programs, governance, number of registries, purpose and data uses, data collection, expenses, funding and interoperability. RESULTS: The response rate was 52 percent. Many registries were self-funded, with 39 percent spending less than $1 million per year, and 32 percent spending $1-9.9 million. The typical registry had three full-time equivalent staff. Registries were frequently used for quality improvement, benchmarking and clinical decision support. 85 percent captured outpatient data. Most registries collected demographics, treatments, practitioner information and comorbidities; 53 percent captured patient-reported outcomes. 88 percent used manual data entry and 18 percent linked to external secondary data sources. Cost, interoperability and vendor management were barriers to continued registry development. CONCLUSIONS: Registries captured data across a broad scope, audited data quality using multiple techniques, and used a mix of automated and manual data capture methods. Registry interoperability was still a challenge, even among registries using nationally accepted data standards.

Isolating influential regions of electrostatic focusing in protein and DNA structure.

Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.

A fragment-based approach to the SAMPL3 Challenge.

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands--regardless of the charged state in the active site--produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.