Background: The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective: The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. Results: The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. Conclusions: The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.

Since its emergence over two decades ago, process mining has flourished as a discipline, with numerous contributions to its theory, widespread practical applications, and mature support by commercial tooling environments. However, its potential for significant organisational impact is hampered by poor quality event data. Process mining starts with the acquisition and preparation of event data coming from different data sources. These are then transformed into event logs, consisting of process execution traces including multiple events. In real-life scenarios, event logs suffer from significant data quality problems, which must be recognised and effectively resolved for obtaining meaningful insights from process mining analysis. Despite its importance, the topic of data quality in process mining has received limited attention. In this paper, we discuss the emerging challenges related to process-data quality from both a research and practical point of view. Additionally, we present a corresponding research agenda with key research directions.

‘Business Process Standardization’ (BPS) is a practice intended to achieve consistency across underlying organizational processes, in support of service-delivery excellence and optimization of costs and benefits. Given the growing importance of BPS, there is need in both research and practice for clearer conceptual understanding of the notion. Following a systematic literature review approach, prior work on Business Process Standardization is synthesized and extended using tool-supported qualitative data analysis techniques, to derive an evidence-based, holistic conceptualization of BPS. We distil seven stages of BPS execution, propose a refined definition for BPS, and identify measures that gauge the several conceptual themes of BPS. The findings provide solid theoretical foundations, and practicable guidelines for future BPS researchers and practitioners.

Objectives: Our aim was to explore the performance of TRPS1 as an immunohistochemical diagnostic marker; find the optimal conditions for its use in breast carcinomas, especially triple-negative breast cancers (TNBCs); and compare its results in carcinomas of a select few organ sites, with an emphasis on gynecologic tumors. Methods: Tissue microarrays from breast carcinomas (n = 197), endometrial adenocarcinomas (n = 69), ovarian tumors (n = 250), vulvar squamous cell carcinomas (n = 97), pancreatic ductal adenocarcinomas (n = 20), and gastric adenocarcinomas (n = 12) were stained with TRPS1 using 2 different conditions (protocol 1: high pH; protocol 2: low pH). Breast carcinomas consisted of hormone receptor (HR)–positive/ERBB2 (formerly HER2 or HER2/neu)–negative (n = 53) samples, HR-positive/ERBB2-positive (n = 6) samples, and TNBCs (n = 138). Results: Comparing TRPS1 results in breast carcinomas vs tumors from other organ sites, the sensitivity of TRPS1 was 91% and 87%, respectively, while the specificity was 66% and 74% for protocol 1 and 2, respectively. For TNBCs vs gynecologic tumors, the sensitivity of TRPS1 was 89% and 85%, respectively, while the specificity was 65% and 73%, respectively. Conclusions: TRPS1 stains approximately 90% of breast carcinomas but also up to 71% of endometrial carcinomas, albeit with a weaker median expression. Our data show that although TRPS1 is a highly sensitive marker for TNBCs, it is not as highly specific as previously reported.