Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies

Securing, Standardizing, and Simplifying Electronic Health Record Audit Logs Through Permissioned Blockchain Technology

Audit logs perform critical functions in electronic health record (EHR) systems. They provide a chronological record of all operations performed in an EHR, allowing health care organizations to track EHR usage, hold system users accountable for their interactions with patient records, detect anomalous and potentially malicious behavior in the system, protect patient privacy, and develop insight into workflows and interactions among system users. However, several problems exist with the way that current state-of-the-art EHR technology handles audit data. Specifically, current systems complicate the collection and analysis of audit logs because they lack an interoperable audit log structure, spread audit log data from different EHR applications across multiple data repositories, and often fail to record all useful information about events in the EHR. Permissioned blockchain technology offers two opportunities to mitigate these issues. First, smart contracts running on the blockchain can impose an interoperable structure on audit log data, both within single health care organizations and across all organizations participating in the network. Second, the blockchain ledger constitutes a consolidated repository for all audit log data at each organization, simplifying the collection of data for analysis. AuditChain, the prototype system I present in this thesis, leverages Hyperleger Fabric\u27s permissioned blockchain technology to address these issues of audit log interoperability, content, structure, and consolidation. Specifically, AuditChain uses the blockchain ledger and smart contracts to standardize audit log content, simplify access to audit log data, and ensure that audit logs contain all necessary and useful information

The application of process mining to care pathway analysis in the NHS

Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

A Knowledge-Constrained Role-Based Access Control model for protecting patient privacy in hospital information systems

Current access control mechanisms of the hospital information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a Knowledge-Constrained Role-Based Access Control (KCRBAC)model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps protect patient privacy by controlling access. Compared with the Role-Based Access Control model, KC-RBAC can effectively protectpatient information according to the results of the experiments

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy

This open access book provides a concise yet comprehensive overview on how to build a quality management program for hematopoietic stem cell transplantation (HSCT) and cellular therapy. The text reviews all the essential steps and elements necessary for establishing a quality management program and achieving accreditation in HSCT and cellular therapy. Specific areas of focus include document development and implementation, audits and validation, performance measurement, writing a quality management plan, the accreditation process, data management, and maintaining a quality management program. Written by experts in the field, Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy: A Practical Guide is a valuable resource for physicians, healthcare professionals, and laboratory staff involved in the creation and maintenance of a state-of-the-art HSCT and cellular therapy program

From Data to Knowledge in Secondary Health Care Databases

The advent of big data in health care is a topic receiving increasing attention worldwide. In the UK, over the last decade, the National Health Service (NHS) programme for Information Technology has boosted big data by introducing electronic infrastructures in hospitals and GP practices across the country. This ever growing amount of data promises to expand our understanding of the services, processes and research. Potential bene�ts include reducing costs, optimisation of services, knowledge discovery, and patient-centred predictive modelling. This thesis will explore the above by studying over ten years worth of electronic data and systems in a hospital treating over 750 thousand patients a year. The hospital's information systems store routinely collected data, used primarily by health practitioners to support and improve patient care. This raw data is recorded on several di�erent systems but rarely linked or analysed. This thesis explores the secondary uses of such data by undertaking two case studies, one on prostate cancer and another on stroke. The journey from data to knowledge is made in each of the studies by traversing critical steps: data retrieval, linkage, integration, preparation, mining and analysis. Throughout, novel methods and computational techniques are introduced and the value of routinely collected data is assessed. In particular, this thesis discusses in detail the methodological aspects of developing clinical data warehouses from routine heterogeneous data and it introduces methods to model, visualise and analyse the journeys that patients take through care. This work has provided lessons in hospital IT provision, integration, visualisation and analytics of complex electronic patient records and databases and has enabled the use of raw routine data for management decision making and clinical research in both case studies

Sisäisen uhan havaitseminen terveydenhuollon käyttölokeista

Sosiaali- ja terveydenhuollossa on siirrytty käyttämään sähköisiä potilastietoja. Potilasturvallisuuden takaamiseksi laki edellyttää keräämään lokitietoja niiden käytöstä. Käyttölokeista voidaan havaita käyttäjien suorittamaa potilastietojen väärinkäyttöä auditoimalla, mutta tietojen suuri määrä vaikeuttaa niiden manuaalista läpikäyntiä. Kun suurista tietomääristä yritetään löytää oleellista tietoa, samankaltaisuuksia ja poikkeavuuksia, voidaan hyödyntää tiedonlouhinta- ja koneoppimistekniikoita. Tekniikat ovat tärkeä osa väärinkäytön ja sisäisen uhan havaitsemiseksi kutsuttuja tutkimusaloja. Tutkielmassa etsittiin terveydenhuoltoon sopivia sisäisen uhan havaitsemismenetelmiä, jotka hyödyntävät käyttölokeja. Tutkimusmenetelmänä havaitsemismenetelmien etsintään käytettiin integroivaa kirjallisuuskatsausta, jonka aineistoon valikoitui 19 laatuarvioitua tieteellistä julkaisua. Sisällytetyt julkaisut vuosilta 2009–2019 kerättiin tietotekniikan alan tietokannoista. Tutkielman keskeisin tulos on itse kirjallisuuskatsaus, jossa esitellään aihealueen aiempia tutkimuksia ja muodostetaan synteesi. Synteesi sisältää tiivistetyn nykytilannekuvauksen sisäisen uhan havaitsemisratkaisuista terveydenhuoltoympäristössä. Toimiva järjestelmä selvittää, viittaako käyttölokitietue, käyttäjä tai potilas väärinkäyttöön. Järjestelmän havaitsemisstrategia hyödyntää yksinkertaisia sääntöjä, hälytysten priorisointia ja vähentämistä, suosittelua, normaalikäytön selitysmallinteita tai läheisyysmittoja. Järjestelmän kannalta tärkeitä tietoja ovat käyttölokit, organisaatio- ja hoitotiedot. Terveydenhuoltoon sopivien havaitsemismenetelmien löytäminen on mahdollista kirjallisuuskatsauksen avulla, vaikka yhtenäisten hakusanojen muodostaminen tuo haasteita. Katsaus osoitti, että soveltuvien menetelmien kokonaisuus on monipuolinen, ja että niiden avulla havaitsemistyötä on todennäköisesti mahdollista tehostaa. Lisäksi sisäisen uhan havaitsemisen tutkimusala on aktiivinen, joten uusia havaitsemisstrategioita voi löytyä lisää lähitulevaisuudessa. On todennäköistä, että terveydenhuoltoympäristön erityispiirteiden vuoksi tulevaisuudenkin ratkaisut nojaavat vahvasti käyttölokeihin. Jatkotutkimuksissa olisi syytä selvittää menetelmien käytännön soveltuvuutta suomalaisessa terveydenhuollossa olemassa olevien järjestelmien rinnalla

Efficacy of Bifidobacterium animalis subsp. lactis (BB-12), B. infantis and Lactobacillus acidophilus (La-5) probiotics to prevent gut dysbiosis in preterm infants of 28+0–32+6 weeks of gestation: a randomised, placebocontrolled, double-blind, multicentre trial: the PRIMAL Clinical Study Protocol

Introduction The healthy ‘eubiosis’ microbiome in infancy is regarded as the microbiome derived from term, vaginally delivered, antibiotic free, breastfed infants at 4–6 months. Dysbiosis is regarded as a deviation from a healthy state with reduced microbial diversity and deficient capacity to control drug-resistant organisms. Preterm infants are highly sensitive to early gut dysbiosis. Latter has been associated with sepsis and necrotising enterocolitis, but may also contribute to long-term health problems. Probiotics hold promise to reduce the risk for adverse short-term outcomes but the evidence from clinical trials remains inconclusive and none has directly assessed the effects of probiotics on the microbiome at high resolution. Methods and analysis A randomised, double blind, placebo-controlled study has been designed to assess the safety and efficacy of the probiotic mix of Bifidobacterium animalis subsp. lactis, B. infantis and Lactobacillus acidophilus in the prevention of gut dysbiosis in preterm infants between 28+0 and 32+6 weeks of gestation. The study is conducted in 18 German neonatal intensive care units. Between April 2018 and March 2020, 654 preterm infants of 28+0–32+6 weeks of gestation will be randomised in the first 48 hours of life to 28 days of once daily treatment with either probiotics or placebo. The efficacy endpoint is the prevention of gut dysbiosis at day 30 of life. A compound definition of gut dysbosis is used: (1) colonisation with multidrug-resistant organisms or gram-negative bacteria with high epidemic potential or (2) a significant deviation of the gut microbiota composition as compared with healthy term infants. Dysbiosis is determined by (1) conventional microbiological culture and (2) phylogenetic microbiome analysis by high-throughput 16S rRNA and metagenome sequencing. Persistence of dysbiosis will be assessed at 12-month follow-up visits. Side effects and adverse events related to the intervention will be recorded. Key secondary endpoint(s) are putative consequences of dysbiosis. A subgroup of infants will be thoroughly phenotyped for immune parameters using chipcytometry. Ethics and dissemination Ethics approval was obtained in all participating sites. Results of the trial will be published in peer-review journals, at scientific meetings, on the website (www.primal-study.de) and via social media of parent organisations. Trial registration number DRKS00013197; Pre-results