Introduction to Artificial Intelligence and Machine Learning for Pathology.

Context.—: Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data.

Payment Reform in the Era of Advanced Diagnostics, Artificial Intelligence, and Machine Learning.

Health care is undergoing a profound transformation driven by an increase in new types of diagnostic data, increased data sharing enabled by interoperability, and improvements in our ability to interpret data through the application of artificial intelligence and machine learning. Paradoxically, we are also discovering that our current paradigms for implementing electronic health-care records and our ability to create new models for reforming the health-care system have fallen short of expectations. This article traces these shortcomings to two basic issues.

Quantifying the competitiveness of the electronic health record market and its implications for interoperability.

Objective: The objective of this study was to quantify both the competitiveness of the EHR vendor market in the United States of America (US) and the degree of fragmentation of individual Medicare beneficiaries' medical records across the differing EHR vendors found in the US healthcare system.

Methods And Materials: We determined the Part A and Part B Medicare-expenditure weighted market shares of EHR vendors and estimated the rate of attestation of meaningful use (MU) for EHRs among Medicare Part A & B providers from 2011 to 2016. Based on these data we calculated the annual Herfindahl-Hirschman Index to quantify the competitiveness of the EHR market as well as the number of vendors individual Medicare beneficiaries' medical records were stored in for the period 2014-2016.

Identifying and Coordinating Care for Complex Patients: Findings from the Leading Edge of Analytics and Health Information Technology.

In the United States, a relatively small proportion of complex patients---defined as having multiple comorbidities, high risk for poor outcomes, and high cost---incur most of the nation's health care costs. Improved care coordination and management of complex patients could reduce costs while increasing quality of care. However, care coordination efforts face multiple challenges, such as segmenting populations of complex patients to better match their needs with the design of specific interventions, understanding how to reduce spending, and integrating care coordination programs into providers' care delivery processes.

A Comparison of Disease Burden Between Twins and Control Pairs in Medicare: Quantification of Heredity's Role in Human Health.

To quantify heredity's effects on the burden of illness in the Medicare population, this study linked information between participants in a research twin registry to a comprehensive set of Medicare claims. To calculate disease categories, the authors used the Centers for Medicare & Medicaid Services Hierarchical Conditions Categories (HCC) model that was developed to risk adjust Medicare's capitation payments to private health care plans based on the health expenditure risk of their enrollees. Using the Medicare database, 2 sets of unrelated but demographically matched control pairs (MCPs) were generated, one specific for the monozygotic twin population and the second specific for the dizygotic twin population.

Temporal variation in patterns of comorbidities in the medicare population.

It is widely accepted that Medicare beneficiaries with multiple comorbidities (ie, patients with combinations of more than 1 disease) account for a disproportionate amount of mortality and expenditures. The authors previously studied this phenomenon by analyzing Medicare claims data from 2008 to determine the pattern of disease combinations (DCs) for 32,220,634 beneficiaries. Their findings indicated that 22% of these individuals mapped to a long-tailed distribution of approximately 1 million DCs.

Integrating pathology and radiology disciplines: an emerging opportunity?

Pathology and radiology form the core of cancer diagnosis, yet the workflows of both specialties remain ad hoc and occur in separate "silos," with no direct linkage between their case accessioning and/or reporting systems, even when both departments belong to the same host institution. Because both radiologists' and pathologists' data are essential to making correct diagnoses and appropriate patient management and treatment decisions, this isolation of radiology and pathology workflows can be detrimental to the quality and outcomes of patient care. These detrimental effects underscore the need for pathology and radiology workflow integration and for systems that facilitate the synthesis of all data produced by both specialties.

The complexity of disease combinations in the Medicare population.

Developing systems of care that address the mortality, morbidity, and expenditures associated with Medicare beneficiaries with multiple diseases would benefit from a greater understanding of the complexity of disease combinations (DCs) found in the Medicare population. To develop estimates of the number of DCs, we performed an observational analysis on 32,220,634 beneficiaries in the Medicare Fee-for-Service claims database based on a set of records containing each beneficiary's Part A and B International Classification of Diseases, 9(th) Revision, Clinical Modification (ICD-9-CM) claims data for the year of 2008. We made 2 simplifying adjustments.

A data review and re-assessment of ovarian cancer serum proteomic profiling.

Background: The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research.