Integration Of Patient Information Research Articles

Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm

A smart healthcare system (SHS) is a health service system that employs advanced technologies such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information and connect people and institutions related to healthcare, thereby actively managing and responding to medical ecosystem needs. Edge computing (EC) plays a significant role in SHS as it enables real-time data processing and analysis at the data source, which reduces latency and improves medical intervention speed. However, the integration of patient information, including electronic health records (EHRs), into the SHS framework induces security and privacy concerns. To address these issues, an intelligent EC framework was proposed in this study. The objective of this study is to accurately identify security threats and ensure secure data transmission in the SHS environment. The proposed EC framework leverages the effectiveness of Salp Swarm Optimization and Radial Basis Functional Neural Network (SS-RBFN) for enhancing security and data privacy. The proposed methodology commences with the collection of healthcare information, which is then pre-processed to ensure the consistency and quality of the database for further analysis. Subsequently, the SS-RBFN algorithm was trained using the pre-processed database to distinguish between normal and malicious data streams accurately, offering continuous monitoring in the SHS environment. Additionally, a Rivest-Shamir-Adelman (RSA) approach was applied to safeguard data against security threats during transmission to cloud storage. The proposed model was trained and validated using the IoT-based healthcare database available at Kaggle, and the experimental results demonstrated that it achieved 99.87 % accuracy, 99.76 % precision, 99.49 % f-measure, 98.99 % recall, 97.37 % throughput, and 1.2s latency. Furthermore, the results achieved by the proposed model were compared with the existing models to validate its effectiveness in enhancing security.

Assessing patient information needs for new antidiabetic medications to inform shared decision-making: A best-worst scaling experiment in China.

Shared decision-making (SDM) is a patient-centred approach to improve the quality of care. An essential requirement for the SDM process is to be fully aware of patient information needs. Our study aimed to assess patient information needs for new antidiabetic medications using the best-worst scaling (BWS) experiment. BWS tasks were developed according to a literature review and the focus group discussion. We used a balanced incomplete block design and blocking techniques to generate choice sets. The final BWS contains 11 attributes, with 6-choice scenarios in each block. The one-to-one, face-to-face BWS survey was conducted among type 2 diabetic patients in Jiangsu Province. Results were analyzed using count-based analysis and modelling approaches. We also conducted a subgroup analysis to observe preference heterogeneity. Data from 539 patients were available for analysis. The most desired information domain was the comparative effectiveness of new antidiabetic medications. It consists of the incidence of macrovascular complications, the length of extended life years, changes in health-related quality of life, the incidence of microvascular complications, and the control of glycated haemoglobin. Of all the attributes, the incidence of macrovascular complications was the primary concern. Patients' glycemic control and whether they had diabetes complications exerted a significant influence on their information needs. Information on health benefits is of critical significance for diabetic patients. Patients have different information needs as their disease progresses. Personalized patient decision aids that integrate patient information needs and provide evidence of new antidiabetic medications are worthy of being established. Before data collection, a pilot survey was carried out among diabetic patients to provide feedback on the acceptability and intelligibility of the attributes.

THROUGH THE LENS OF PRINCIPLED DATA PRACTICE A GROUNDBREAKING EXPLORATION INTO ETHICAL HEALTHCARE PLATFORMS

Fundamental Ethical Principles of Data Privacy and Security and how Healthcare Data Platforms Ensure the Confidentiality and Integrity of Patient Information, Safeguarding it Against Unauthorized Access and Cyber Threats. In the last years, there has been a considerable increase in the usage of electronic health records (EHRs). Credit card numbers, full name, bank account numbers, social security numbers, street addresses, phone numbers, passport numbers, treatments, and medical histories are all private patient information that may be found in health information systems. This information must be safeguarded against manipulation and fraud by other parties. EHRs are anticipated to boost healthcare delivery effectiveness, enhance the standard of care, and reduce mounting budgetary strain. Despite these advantages, there is a possibility that security issues with EHRs compromise patient data confidentiality and privacy. This research paper focuses on the ethical principles—privacy, ownership, transparency, data security, informed consent, data minimization, purpose limitation, and accountability—to help guarantee that patient databases maintain the confidentiality and integrity of patient information while protecting it from unapproved access and online threats. Additionally, numerous systems for privacy and integrity, such as vulnerability analyses and role-based access control, support the objectives of contemporary healthcare while preserving patient privacy.

The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation.

To provide high-quality data for coronavirus disease 2019 (COVID-19) research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart. Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19-positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a natural language processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR. The concepts for COVID-19-positive cohort, COVID-19 index date, COVID-19-related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the positive predictive value in the prepandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes toward using DAPR for chart review. They assessed that the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

The Diagnostic Efficacy of an App-based Diagnostic Health Care Application in the Emergency Room: eRadaR-Trial. A prospective, Double-blinded, Observational Study

To evaluate the diagnostic accuracy of the app-based diagnostic tool Ada and the impact on patient outcome in the emergency room (ER). Artificial intelligence-based diagnostic tools can improve targeted processes in health care delivery by integrating patient information with a medical knowledge base and a machine learning system, providing clinicians with differential diagnoses and recommendations. Patients presenting to the ER with abdominal pain self-assessed their symptoms using the Ada-App under supervision and were subsequently assessed by the ER physician. Diagnostic accuracy was evaluated by comparing the App-diagnoses with the final discharge diagnoses. Timing of diagnosis and time to treatment were correlated with complications, overall survival, and length of hospital stay. In this prospective, double-blinded study, 450 patients were enrolled and followed up until day 90. Ada suggested the final discharge diagnosis in 52.0% (95% CI [0.47, 0.57]) of patients compared with the classic doctor-patient interaction, which was significantly superior with 80.9% (95% CI [0.77, 0.84], P <0.001). However, when diagnostic accuracy of both were assessed together, Ada significantly increased the accuracy rate (87.3%, P <0.001), when compared with the ER physician alone. Patients with an early time point of diagnosis and rapid treatment allocation exhibited significantly reduced complications ( P< 0.001) and length of hospital stay ( P< 0.001). Currently, the classic patient-physician interaction is superior to an AI-based diagnostic tool applied by patients. However, AI tools have the potential to additionally benefit the diagnostic efficacy of clinicians and improve quality of care.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

The first step is recognizing there is a problem: a methodology for adjusting for variability in disease severity when estimating clinician performance

BackgroundAdoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others.MethodsWe propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions.ResultsOur metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (β = 0.63, 95% confidence interval 0.46–0.80, p < 7 × 10− 5). Non-PCCM physicians recognized ARDS cases less frequently and expressed greater satisfaction with the ability to get the information needed for making an ARDS diagnosis (p < 5 × 10− 4), suggesting that lower performing clinicians may be less aware of institutional barriers.ConclusionsWe present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.

The life and death of confidentiality: a historical analysis of the flows of patient information

Health data can contain sensitive information. People who consult a doctor seek help on issues that matter to them: they typically expect some form of confidentiality. However, the notion and practices of confidentiality have changed dramatically over time. In this article, we trace the history of confidentiality in the Danish healthcare system, which has one of the world’s most integrated patient information infrastructures. Building on an analysis of legal and political documents dating back to the late seventeenth century, we show that confidentiality originated as a social phenomenon that helped build trust in healthcare professionals and gradually developed into an idiom of citizens rights. Lately, confidentiality has given way to more technocratic forms of data protection. As the political, legal and technological reality, which the idea of confidentiality once referred to, has radically changed, we argue that confidentiality has become what Ulrik Beck has called a ‘zombie category’—a notion that lives on even if its content has passed away. If confidentiality has become a zombie concept, we suggest it is time to discuss what may take its place so that patient interests are protected in the current political economy of health data.

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

Introduction: Physicians spend an ever-rising amount of time to collect relevant information from highly variable medical reports and integrate them into the patient’s health condition. Objectives: We compared synoptic reporting based on data elements to narrative reporting in order to evaluate its capabilities to collect and integrate clinical information. Methods: We developed a novel system to align medical reporting to data integration requirements and tested it in prostate cancer screening. We compared expenditure of time, data quality, and user satisfaction for data acquisition, integration, and evaluation. Results: In a total of 26 sessions, 2 urologists, 2 radiologists, and 2 pathologists conducted the diagnostic work-up for prostate cancer screening with both narrative reporting and the novel system. The novel system led to a significantly reduced time for collection and integration of patient information (91%, p < 0.001), reporting in radiology (44%, p < 0.001) and pathology (33%, p = 0.154). The system usage showed a high positive effect on evaluated data quality parameters completeness, format, understandability, as well as user satisfaction. Conclusion: This study provides evidence that synoptic reporting based on data elements is effectively reducing time for collection and integration of patient information. Further research is needed to assess the system’s impact for different patient journeys.

Iterative heuristic design of temporal graphic displays with clinical domain experts.

Conventional electronic health record information displays are not optimized for efficient information processing. Graphical displays that integrate patient information can improve information processing, especially in data-rich environments such as critical care. We propose an adaptable and reusable approach to patient information display with modular graphical components (widgets). We had two study objectives. First, reduce numerous widget prototype alternatives to preferred designs. Second, derive widget design feature recommendations. Using iterative human-centered design methods, we interviewed experts to hone design features of widgets displaying frequently measured data elements, e.g., heart rate, for acute care patient monitoring and real-time clinical decision-making. Participant responses to design queries were coded to calculate feature-set agreement, average prototype score, and prototype agreement. Two iterative interview cycles covering 64 design queries and 86 prototypes were needed to reach consensus on six feature sets. Interviewers agreed that line graphs with a smoothed or averaged trendline, 24-h timeframe, and gradient coloring for urgency were useful and informative features. Moreover, users agreed that widgets should include key functions: (1) adjustable reference ranges, (2) expandable timeframes, and (3) access to details on demand. Participants stated graphical widgets would be used to identify correlating patterns and compare abnormal measures across related data elements at a specific time. Combining theoretical principles and validated design methods was an effective and reproducible approach to designing widgets for healthcare displays. The findings suggest our widget design features and recommendations match critical care clinician expectations for graphical information display of continuous and frequently updated patient data.

Hospital adoption of multiple health information exchange approaches and information accessibility

Hospital engagement in electronic health information exchange (HIE) has increased over recent years. We aimed to 1) determine the change in adoption of 3 types of information exchange: secure messaging, provider portals, and use of an HIE; and 2) to assess if growth in each approach corresponded to increased ability to access and integrate patient information from outside providers. Panel analysis of all nonfederal, acute care hospitals in the United States using hospital- and year-fixed effects. The sample consisted of 1917 hospitals that responded to the American Hospital Association Information Technology Supplement every year from 2014 to 2016. Adoption of each approach increased by 9-15 percentage points over the study period. The average number of HIE approaches used by each hospital increased from 1.0 to 1.4. Adoption of each approach was associated with increased likelihood that providers routinely had necessary outside information of 4.2-12.7 percentage points and 4.5-13.3 percentage points increase in information integration. Secure messaging was associated with the largest increase in both. Adoption of 1 approach increased the likelihood of having outside information by 10.3 percentage points, while adopting a second approach further increased the likelihood by 9.5 percentage points. Trends in number of approaches and integration were similar. No single HIE tool provided high levels of usable, integrated health information. Instead, hospitals benefited from adopting multiple tools. Policy initiatives that reduce the complexity of enabling high value HIE could result in broader adoption of HIE and use of information to inform care.

Securing mHealth Applications Using IoTsecM Security Modelling: Dentify.Me mApp Case Study for Urgent Care Management

Mobile devices and the Internet of Things (IoT) are revolutionizing today’s digital sectors, including healthcare. eHealth services delivery enables integrated mHealth care and informed-decision making for emergency medical services, especially in the event of disasters when every second could mean the difference between life or death. The risk of cyber-attacks directed to mHealth applications can compromise the availability and integrity of patient information, crippling care mobility and sometimes threatening patients’ lives if decisions are made based on invalid information. Such risks can be treated by considering appropriate information security controls at the early stages of the mobile Application (mApp) development lifecycle for mHealth model of care. However, most developers consider security at a later stage, and even if they do, there is a lack of an appropriate tool to help them represent security requirements in design models. This has proven to be bad practice, resulting in insecure mApp development. This paper aims to bridge this gap by equipping analysts with the tool necessary to identify risks and treat them while designing the application. Therefore, we propose the approach referred to as Internet of Things Security Modelling (IoTsecM) for mApp security modelling in mHealth. IoTsecM is a UML extension to model identified security controls against possible attacks to guarantee the existence of a security analysis and security mechanisms. Results show that IoTsecM, first, allows mHealth designers to apply and depict non-functional security requirements with the functional requirements. Second, its annotation illustrates meaningful information security requirements at early design stages as part of the mHealth application development lifecycle and not afterwards.

Development and Assessment of an Integrated Patient Information, Education and Support Program in Head and Neck Cancer - Comprehensive Head and Neck Cancer Education and Support (CHANCES) Program

Interrelated factors at the patient, provider, and information levels can have a detrimental impact on patient understanding of health information and subsequent health outcomes. We developed a comprehensive, multiformat education program for patients with head and neck cancer (HNC) and healthcare professionals (HCP) involved in their care. Aims: To establish a HNC information platform accessible and acceptable to HCP and patients incorporating continuous quality improvement and new evidence as it emerges. A multi-stage development and revision process was undertaken. Stage 1: Cross-sectional stakeholder information needs survey. Stage 2: Content development: Multidisciplinary expert panel (n=20) developed content using an iterative review process. Stage 3: Multiplatform resource build; Stage 4. Usability testing; Stage 5. User acceptability testing. In a cross sectional survey of HCP perceptions of HNC resources 112 /648 (17%) clinicians responded with 85% using written information as their primary mode of patient education. They indicated need for improved information on pain management, risk factors, survivorship, and side effects. The majority (66%) indicated a preference for internet-based resources. A total of 596 patients responded to the survey of information needs, with most reported receiving sufficient information about disease process and treatment. Fewer reported information about psychosexual health (56%) or patient support groups (56%). Most patients wanted access informative via multiple modes of delivery. Information addressing 10 types of HNC from diagnosis through treatment to survivorship was developed, reviewed a minimum of three times each, and underwent assessment of readability before being finalized. Website was developed to present the information, animations, video vignettes, and downloadable information booklets. Usability assessment in 15/56 patients, 90% with high health literacy, indicate information presentation, order, and explanation of medical jargon was very clear (90%). Access to both written and electronic form as preferred by 33% of participants. Most patients (90%) felt prepared about their cancer diagnosis and treatment after reading the information resource. The CHANCES program is an evidence-based HNC information resource acceptable, accessible, and understandable to patients and to HCP. Longer-term evaluation is required to assess its impact on health outcomes and resource use.

An Assessment of the Interoperability of Electronic Health Record Exchanges Among Hospitals and Clinics in Taiwan.

BackgroundThe rapid aging of the Taiwanese population in recent years has led to high medical needs for the elderly and increasing medical costs. Integrating patient information through electronic health records (EHRs) to reduce unnecessary medications and tests and enhance the quality of care has currently become an important issue. Although electronic data interchanges among hospitals and clinics have been implemented for many years in Taiwan, the interoperability of EHRs has not adequately been assessed.ObjectiveThe study aimed to analyze the efficiency of data exchanges and provide suggestions for future improvements.MethodsWe obtained 30 months of uploaded and downloaded data of EHRs among hospitals and clinics. The research objects of this study comprised 19 medical centers, 57 regional hospitals, 95 district hospitals, and 5520 clinics. We examined 4 exchange EHR forms: laboratory test reports, medical images, discharge summaries, and outpatient medical records. We used MySQL (Oracle Corporation) software (to save our data) and phpMyAdmin, which is a Personal Home Page program, to manage the database and then analyzed the data using SPSS 19.0 statistical software.ResultsThe quarterly mean uploaded volume of EHRs among hospitals was 52,790,721 (SD 580,643). The quarterly mean downloaded volume of EHRs among hospitals and clinics was 650,323 (SD 215,099). The ratio of uploaded to downloaded EHRs was about 81:1. The total volume of EHRs was mainly downloaded by medical centers and clinics, which accounted for 53.82% (mean 318,717.80) and 45.41% (mean 269,082.10), respectively, and the statistical test was significant among different hospital accreditation levels (F2=7.63; P<.001). A comparison of EHR download volumes among the 6 National Health Insurance (NHI) branches showed that the central NHI branch downloaded 11,366,431 records (21.53%), which was the highest, and the eastern branch downloaded 1,615,391 records (3.06%), which was the lowest. The statistical test among the 6 NHI branches was significant (F5=8.82; P<.001). The download volumes of laboratory tests reports and outpatient medical records were 26,980,425 (50.3%) and 21,747,588 records (40.9%), respectively, and were much higher than medical images and discharge summaries. The statistical test was also significant (F=17.72; P<.001). Finally, the download time showed that the average for x-rays was 32.05 seconds, which was the longest, and was 9.92 seconds for electrocardiogram, which was the shortest, but there was no statistically significant difference among download times for various medical images.ConclusionsAfter years of operation, the Electronic Medical Record Exchange Center has achieved the initial goal of EHR interoperability, and data exchanges are running quite stably in Taiwan. However, the meaningful use of EHRs among hospitals and clinics still needs further encouragement and promotion. We suggest that the government’s leading role and collective collaboration with health care organizations are important for providing effective health information exchanges.

DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.

Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of these data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our knowledge, there are no previous studies addressing the automatic detection of outlier prescriptions, regarding dosage and frequency. In this paper, we propose an unsupervised method, called density-distance-centrality (DDC), to detect potential outlier prescriptions. A dataset with 563 thousand prescribed medications was used to assess our proposed approach against different state-of-the-art techniques for outlier detection. In the experiments, our approach achieves better results in the task of overdose and underdose detection in medical prescriptions, compared to other methods applied to this problem. Additionally, most of the false positive instances detected by our algorithm were potential prescriptions errors.

The expanding role of patients in integrated cancer network governance.

106 Background: The expanding role of patients (including family) at all levels of healthcare systems is increasingly recognized to favour changes in cancer services delivery. The province of Quebec (Canada) is no exception and promotes patients involvement in governance activities such as the implementation of the National cancer program, local integrated cancer networks, practice guidelines, and patient information material. Generally speaking, network governance refers to the steering process carried out to ensure goals’ achievement. However, we lack sufficient understanding of how patients contribute to this process, at local and national levels. This presentation seeks to report on patients involvement in governance activities within integrated cancer networks. Methods: Design: Qualitative data comprising semi‐structured interviews, focus groups and review of documents. Setting: Four Integrated Local Cancer Networks (ILCN) in Quebec purposively selected to provide variation in size, geographic location and time since implementation of the ILCN. Participants: Key stakeholders including policy-makers (n = 8), managers (n = 56), clinicians (n = 39), patients and their family members (n = 28). Analysis: Content analysis using open-ended coding. Results: This study shows the added value of the formalization of patients involvement in the governance committees at local and national levels of the cancer network. It illustrates the operationalization of the political will to translate the Quebec cancer program ideas into concrete patient-centred practice. Enlightening patients experience through discussion leads to shared decision making, building a “community of patients”, the diffusion of supportive tools, and the development, use and interpretation of performance indicators. Conclusions: Patients involvement is a major, but manageable, change in the integration of care across the cancer continuum. Formalizing the expending role of patients in local and national levels governance committees narrows the macro (hierarchical) and micro (relational) governance divide, which in turn contributes to patient-centred and integrated cancer care.

Measuring the value of the Health Care Home

Introduction: The primary care environment is demanding new models of care driven by population and workforce demographics, but also by technology changing the way we live our lives and therefore our expectations of service delivery. The outcomes gained from a strong primary care system are well documented, however the sustainability of this sector, particularly in rural areas is under threat. The Pinnacle Midlands Health Network ‘Health Care Home’ (HCH) model of care transforms the role of general practice as the integrator within a community and health care system, whilst at the same time shifting the control of healthcare more towards the individual and their family. It shifts the primary health care eco-system from largely a reactive, provider-centric system to one that proactively plans and delivers care using the ‘one assessment, one plan and one team’ concept of care. Drives towards integrated care have often been hampered by concentration on one aspect of service delivery without making the necessary alignments to other elements of the system. Creating a HCH environment cracks the well-documented barriers to integrated care, building leadership and clinical capacity, changing professional custom and practice, expanding roles, establishing integrated patient information systems and aligning funding and contractual models. Pinnacle is partnering with other organisations around New Zealand to develop HCHs across the country. However the exact nature of what constitutes a HCH in the New Zealand setting, and what might form minimum standards for such are undefined. We sought to create a list of criteria that would define a HCH, and enable measurement of progress towards that status. Method: The literature was searched for all mentioned aspects of HCHs. The implementation documentation for each existing HCH in New Zealand was searched to identify all changed components. Each was mapped onto a four point scale, from no progress toward HCH to full implementation of HCH. These were tested and refined in workshops of HCH providers and commissioners covering a variety of implementation sites and HCH styles. The final version was then used by HCH providers to provide a historical score for their practices over time on each dimension. Results: The HCH Implementation Tool was successfully developed and deployed. Results from the deployment will be presented at the Congress. It has already proved useful for practices starting on their transformation to measure their progress and how far they have to go. Conclusions: The Health Care Home model demonstrates improved outcomes for patient experience, workforce satisfaction and system efficiency. It is possible to define and measure the changes leading to a primary care practice becoming a Health Care Home and the extent of its integration with other services, allowing quantification in addition to common qualitative measures

Health Care Home - what is it?

Introduction: The primary care environment is demanding new models of care driven by population and workforce demographics, but also by technology changing the way we live our lives and therefore our expectations of service delivery. The outcomes gained from a strong primary care system are well documented, however the sustainability of this sector, particularly in rural areas is under threat. The Pinnacle Midlands Health Network ‘Health Care Home’ (HCH) model of care transforms the role of general practice as the integrator within a community and health care system, whilst at the same time shifting the control of healthcare more towards the individual and their family. It shifts the primary health care eco-system from largely a reactive, provider-centric system to one that proactively plans and delivers care using the ‘one assessment, one plan and one team’ concept of care. Drives towards integrated care have often been hampered by concentration on one aspect of service delivery without making the necessary alignments to other elements of the system. Creating a HCH environment cracks the well-documented barriers to integrated care, building leadership and clinical capacity, changing professional custom and practice, expanding roles, establishing integrated patient information systems and aligning funding and contractual models. Pinnacle MHN has 14 practices who have adopted the model and another seven in the immediate pipeline with one locality delivering an integrated health and social care model. Pinnacle is partnering with other organisations around New Zealand to develop HCHs across the country. However the exact nature of what constitutes a HCH in the New Zealand setting, and what might form minimum standards for such are undefined. We sought to create a list of criteria that would define a HCH, and enable measurement of progress towards that status. Method: The literature was searched for all mentioned aspects of HCHs. The implementation documentation for each existing HCH in New Zealand was searched to identify all changed components. Each was mapped onto a four point scale, from no progress toward HCH to full implementation of HCH. These were tested and refined in workshops of HCH providers and commissioners covering a variety of implementation sites and HCH styles. The final version was then used by HCH providers to provide a historical score for their practices over time on each dimension. Results: The HCH Implementation Tool was successfully developed and deployed. Results from the deployment will be presented at the Congress. It has already proved useful for practices starting on their transformation to measure their progress and how far they have to go. Conclusions: It is possible to define and measure the changes leading to a primary care practice becoming a Health Care Home and the extent of its integration with other services, allowing quantification of an otherwise qualitative measure

An Integrated Patient Information and In-Home Health Monitoring System Using Smartphones and Web Services.

Modern healthcare systems are undergoing a paradigm shift from in-hospital care to in-home monitoring, leveraging the emerging technologies in the area of bio-sensing, wireless communication, mobile computing, and artificial intelligence. In-home monitoring promises to significantly reduce healthcare spending by preventing unnecessary hospital admissions and visits to healthcare professionals. Most of the in-home monitoring systems, proposed in the literature, focus on monitoring a set of specific vital signs. However, from the perspective of caregivers it is infeasible to maintain a collection of specialized monitoring systems. In this paper, we view the problem of in-home monitoring from the perspective of caregivers and present a framework that supports various monitoring capabilities while making the complexity transparent to the end users. The essential idea of the framework is to define a 'general purpose architecture' where the system specifies a particular protocol for communication and makes it public. Then any bio-sensing system can communicate with the system as long as it conforms to the protocol. We then argue that as the system grows in terms of number of patients and bio-sensing systems, artificial intelligence technologies need to be employed for patients' risk assessment, prioritization, and recommendation. Finally, we present an initial prototype of the system designed according to the proposed framework.

Research on Traditional Chinese Food Medical Four Diagnostic Information Collection Platform Design Using Cloud Storage Architecture

A Traditional Chinese Food Medical (TCFM) four diagnostic information collection platform for dietary obesity is designed based on cloud storage architecture. The patient information recorded in the collection platform may comes from three different information collection devices: computer, image acquisition and cell phone. An application with friendly graphic user interface is implemented on computer devices to collect text-class food medical information; image acquisition is a closed collector box which can provide no natural light environment and acquire the face, tongue, hands and other image without natural light affection; a mobile application is designed for patients to record daily health information in their cell phone and transmitted to the information collection platform. In order to efficiently integrate patient information collected by different devices, information collection platform based on cloud storage architecture is designed, the architecture of cloud storage is layered and cooperative and the key technologies including deployment, storage scheme, data organization is considered.