Disease Management System Research Articles

Nurses’ Experience of End of Life Care in a New Infectious Disease Crisis: A Qualitative Thematic Analysis

This study aims to explore and understand nurses’ end of life experiences during care for critical COVID-19 patients. In-depth interviews were conducted with 11 nurses working at a dedicated COVID-19 hospital in ‘D’ Metropolitan City. The 11 nurses all had experience in end of life care for critical COVID-19 patients. Collected data was examined using qualitative thematic analysis. The results showed the four categories were: trauma from end of life care, overwhelming nursing responsibilities for critical COVID-19 patients, role as guardians and supporters of critical COVID-19 patients, growth of professional identity and professional competency as a nurse. An inadequate infectious disease management system burdened nursing practitioners with many responsibilities during the early stages of the pandemic. However, This experience gave nurses an opportunity to re-think the value of caring and re-establish their professional identity. Institutional support and social interest are required to improve the quality of end of life care and work processes and provide psychological support and recovery for nursing practitioners.

The Distribution of Fecal Contamination in an Urbanized Tropical Lake and Incidence of Acute Diarrheal Disease

Aquatic ecosystems of tropical countries are vulnerable to fecal contamination that could cause spikes in the incidences of acute diarrheal disease (ADD) and challenge public health management systems. Vembanad lake, situated along the southwest coast of India, was monitored for one year (2018–2019). Escherichia coli, an indicator of fecal contamination, was prevalent in the lake throughout the year. Multiple antibiotic resistance among more than 50% of the E. coli isolates adds urgency to the need to control this contamination. The high abundance of E. coli and incidence of ADD were recorded during the early phase of the southwest monsoon (June–July), prior to the once-in-a-century floods that affected the region in the later phase (August). The extent of inundation in the low-lying areas peaked in August, but E. coli in the water peaked in July, suggesting that contamination occurred even prior to extreme flooding. During the COVID-19-related lockdown in March–May 2021, fecal contamination in the lake and incidence of ADD reached minimum values. These results indicate the need for improving sewage treatment facilities and city planning in flood-prone areas to avoid the mixing of septic sewage with natural waters during extreme climate events or even during the normal monsoon.

Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions

Identifying corn diseases under field conditions is crucial for implementing effective disease management systems. Deep learning (DL)-based plant disease identification using deep neural networks (DNN) has been successfully implemented in recent years. Recent work suggests DL models trained on lab-acquired image data do not generalize to similar accuracy levels for identifying diseases in the field. Additionally, most studies have not evaluated the generalizability of DL models for identifying plant diseases from various datasets and diverse imaging conditions. This study evaluates how well DL models generalize across different datasets and environmental conditions for identifying plant diseases using five datasets consisting of foliar disease images in corn: namely PlantVillage, PlantDoc, Digipathos, northern leaf blight (NLB) dataset, and a custom acquired CD&S dataset. Multiple DL-based image classification models were trained and evaluated using different dataset combinations. Transfer learning was utilized using five different pre-trained DNN architectures: InceptionV3, ResNet50, VGG16, DesneNet169, and Xception, for conducting four different experiments. After training the models, images for distinct corn diseases from different datasets were used as testing data for evaluating the generalization ability of each DL model. It was observed that the DenseNet169 model performed the best. The highest generalization accuracy of 81.60% was observed when the DenseNet169 model was trained using red, green, blue, and alpha (RGBA) images from CD&S corn disease dataset with removed backgrounds. In addition, 77.50% to 80.33% accuracy was achieved for the PlantVillage dataset when combined with field-acquired images from either the PlantDoc or the CD&S dataset. The results suggest that background removal using RGBA images from CD&S dataset or augmentation of field and lab data improves the generalization performance of DL models and could be used for developing field-deployable disease management systems.

Patient Perspectives of Chronic Disease Management and Unmet Care Needs in South Korea: A Qualitative Study.

Understanding and incorporating patients' perspectives are necessary to address the emerging challenge of chronic disease management. Our study examined patients' perceptions and experiences for the current chronic disease management system in South Korea. Focus group interviews were conducted on 23 patients and 11 themes emerged by qualitative content analysis. The participants experienced in terms of provider-patient interaction: doctors only prescribe medicine, doctors who provide conventional advice, doctors who do not respect the patients' opinion, long wait times and inadequate consultations, lack of personalized care, and freedom to select another doctor. They also experienced in their community and health system: struggling alone, commercial media and folk remedies, lack of IT technologies for care, demanding visiting services, and lack of collaboration in the community. We found that patients needed comprehensive and personalized care, respect from providers, and self-management support and collaborated care with the community using information technologies advancement. Our findings suggest that a fundamental change in the South Korean healthcare system paradigm is required for successful chronic care, including payment and healthcare delivery systems.

Improvement of antibiotic use in sore throat, tonsillitis and acute otitis media in Palestine: a prospective full cycle audit

Antibiotics are commonly prescribed for upper respiratory tract infections (URTIs) which are generally caused by viral pathogens and do not require antibiotics under most circumstances. Adherence to the international guidelines regarding the necessity of prescribing antibiotics and the selection of antibiotics used for these illnesses have never been reviewed in West Bank. This study was a prospective full cycle audit and re-audit done in West Bank, Palestine. Audit and re-audit phases involved short interviews. The intervention phase included surveys, oral presentations, and development of mobile/web applications. p values of < 0.05 were considered significant. A total of 297 cases were reviewed during the audit and reaudit phases. These cases included tonsillitis (56.9%), sore throat (29.6%) and acute otitis media (13.5%). Improvements in the percentages of correct antibiotics prescriptions in the reaudit phase were noticed, including + 6.5% for correct direct antibiotics prescriptions, + 44.4% for correct backup antibiotics prescriptions, and + 63.4% for correct no antibiotics prescriptions. Improvements in the percentages of prescribing the correct choice of antibiotic (+ 41.4%) and frequency, dosage and duration (+ 13.3%) were also recorded. There was inadequate adherence to the international guidelines of antibiotic prescription for URTIs indicating a possible national problem. There is an evident trend toward using the second-line antibiotics for URTI. Improvements were observed in antibiotic prescribing patterns over a four months period. Therefore, our study's improvement strategies and approaches can be extended to other disease management systems and locations.

Understanding support systems for Parkinson's disease management in community settings: A cross-national qualitative study.

Health and social care systems face difficulties in managing multimorbidity, disease burden and complex needs in long-term conditions such as Parkinson's disease. This study aimed to develop a European understanding of how health and social care professionals can collaborate with stakeholders from different organizations and sectors to enhance the management of Parkinson's disease in a community setting by identifying the existing gaps in this process and how people with Parkinson's disease and their family carers could benefit from these partnerships. A mixed-methods sequential study was conducted in Denmark, Norway, Spain and the United Kingdom. The findings from the qualitative phase are presented. Individual semistructured interviews were analysed using Braun's and Clarke's thematic analysis. A meta-ethnography approach was used to analyse and synthesize cross-national findings. A total of 41 healthcare professionals and 39 stakeholders from different disciplines and sectors were interviewed in the four countries. The participants acknowledged a lack of awareness of available resources and poor communication between the different support systems in the management of Parkinson's disease. To promote multiagency collaborations, the participants highlighted the need to organize services along the Parkinson's disease journey, patient involvement and strategic involvement of carers in organizing resources and Parkinson's disease care pathways. According to the participants, the benefits from multiagency partnerships could lead to an enhanced continuity of care and specialized knowledge, mobilization of resources in the community, personalized support and improved access to services. Policymakers are called upon to create formal structures that facilitate multisectoral collaborations to promote an integrated system of care for the management of Parkinson's disease in the community. To address this challenge, we propose five strategies showing how organizations can work together to optimize the use of resources and enhance the management of Parkinson's disease throughout the illness trajectory. Patient and Public Involvement groups made up of stakeholders, healthcare professionals, patients with Parkinson's disease and family carers participated in the design of the study, the development of the interview guides and the validation of the findings.

Importance of full-cycle management for diabetic retinopathy

Diabetic retinopathy (DR), one of the chronic complications of diabetes, is a serious and irreversible blinding disease. It is difficult to detect in the early stage, to control in the progressive stage, to operate in the advanced stage of DR. Recently, the "14th Five-year plan" for National Eye Health proposed to "improve the management mode of chronic eye disease, and build a chronic disease management system". The project team used artificial intelligence technology based on cloud platform, joint outpatient service, virtual ward to explore the comprehensive management of DR from the aspects of early screening, multidisciplinary collaborative diagnosis and treatment, and refined blood glucose management during perioperative period. In the future, it is urgent to integrate DR chronic disease management with other systemic chronic diseases to reduce the blindness caused by DR.

Neem: A Novel Biocide for Pest and Disease Control of Plants

Plant extracts have recently captivated scientists’ attention as a potential source of biocide for plant protection. This is due to the health and environmental risks associated with the widespread use of synthetic chemicals with adverse effects on humans, nontarget organisms, and the agroecosystem. As a result, more environmentally friendly and safe alternative approaches to synthetic compounds are desirable. Neem (Azadirachta indica) has been identified as a promising biocontrol agent with low toxicity and high efficacy among several plant products for potential chemotherapeutic elements in plant pest and disease management systems. The biocidal potency of neem is attributed to its azadirachtin active ingredient, which impacts some metabolic processes in insects such as protein synthesis, changes in biological fitness, impaired sexual communication, and chitin synthesis. This systematic review intends to gather all the available scientific data regarding the application of neem and its formulations in pest and disease control of plants. The PRISMA (preferred reporting items for systematic reviews and meta-analysis) framework was employed in collecting data for the study. Findings from this review study have demonstrated the use of neem as an effective biocontrol agent for plants’ pests and diseases and have provided a solid foundation for future studies on the plant.

Sentinel surveillance of SARS-CoV-2 rates and equity impacts using labor and delivery patients in Phoenix, Arizona

Proactive management of SARS-CoV-2 requires timely and complete population data to track the evolution of the virus and identify at risk populations. However, many cases are asymptomatic and are not easily discovered through traditional testing efforts. Sentinel surveillance can be used to estimate the prevalence of infections for geographical areas but requires identification of sentinels who are representative of the larger population. Our goal is to evaluate applicability of a population of labor and delivery patients for sentinel surveillance system for monitoring the prevalence of SARS-CoV-2 infection. We tested 5307 labor and delivery patients from two hospitals in Phoenix, Arizona, finding 195 SARS-CoV-2 positive. Most positive cases were associated with people who were asymptomatic (79.44%), similar to statewide rates. Our results add to the growing body of evidence that SARS-CoV-2 disproportionately impacts people of color, with Black people having the highest positive rates (5.92%). People with private medical insurance had the lowest positive rates (2.53%), while Medicaid patients had a positive rate of 5.54% and people without insurance had the highest positive rates (6.12%). With diverse people reporting for care and being tested regardless of symptoms, labor and delivery patients may serve as ideal sentinels for asymptomatic detection of SARS-CoV-2 and monitoring impacts across a wide range of social and economic classes. A more robust system for infectious disease management requires the expanded participation of additional hospitals so that the sentinels are more representative of the population at large, reflecting geographic and neighborhood level patterns of infection and risk.

만성폐쇄성폐질환 환자의 자가관리 경험에 대한 질적메타합성

This study aimed to comprehensively review the self-management experiences of patients with chronic obstructive pulmonary disease(COPD) through meta-synthesis of qualitative studies. The method of this study was applied to Meta-ethnography of Noblit and Hare. Five databases were used to search the literature: PubMed, CINAHL, RISS, DBpia, National Assembly Library and searching keyword were ‘COPD’, ‘self management’, ‘experiences’, and ‘qualitative research’. 371 references were identified and relating to six qualitative studies were included in the review. Three themes were synthesized: “Perceived severity of symptoms”, “Coping strategies” and “Support system of disease management”. Through this study, it was possible to improve the understanding of self management experiences of COPD patients. The results of this study made grounds of a coping strategies for developing self-management programs of COPD patients.

A case-control study of acute hepatitis A in South Korea, 2019.

We aimed to reconfirm the source of hepatitis A virus (HAV) infection through epidemiological and genotype investigations of individual cases in a 2019 outbreak in South Korea. We investigated food intake histories, associations with hepatitis A, and genotypes of HAV in 31 patients with hepatitis aged 20 to 49 years registered in the integrated disease and health management system during December 1-7, 2019 (case group) and in 35 sex- and agematched people without a history of HAV vaccination or infection among patients' families and colleagues (control group). The consumption of salted clams was a significant factor (odds ratio, 4.33; 95% confidence interval, 1.32-14.18) in the risk factor analysis of food intake history. HAV genotypes were analyzed in 24 of 31 patients. Type IA and type IIIA were found in 23 and 1 cases, respectively. Salted clams are considered to have been the source of HAV infection at 49 weeks of the HAV outbreak in 2019; this result was consistent with that of a previous epidemiological investigation conducted by the Korea Disease Control and Prevention Agency in September 2019. Therefore, monitoring of the production and distribution of salted clams needs to be continued.

The coronavirus disease 2019 pandemic and chronic diseases

Background: Chronic diseases contribute to 74% of annual global deaths. The coronavirus disease (COVID-19) pandemic further aggravated the burden of chronic diseases. The reasons for this adverse impact must be elucidated to develop an appropriate response.Current Concepts: COVID-19 increases the burden of chronic disease in three ways. First, chronic disease comorbidities are associated with a higher severity and fatality rate in patients with COVID-19. Second, the post COVID-19 condition results in chronic health problems. Lastly, a disruption in the system for chronic disease screening and care was observed during the COVID-19 pandemic.Discussion and Conclusion: Strong efforts are needed to mitigate the impact of the COVID-19 pandemic on chronic diseases, and to re-establish an effective system for chronic disease management.

Telehealth care system for chronic disease management of middle-aged and older adults in remote areas.

With the global spread of the coronavirus disease pandemic, governments have become more interested in applying telehealth technology in 2020. This study integrates the technology acceptance model and information system success model to explore the influence of satisfaction, attitudes, and continued use of telehealth systems among middle-aged and older people in remote areas. The study participants were patients over 40years (inclusive) who lived in remote townships in eastern Taiwan, were diagnosed with diabetes or hypertension, and used the telehealth care system. In total, 545 questionnaires were returned, resulting in a recovery rate of 99%. Accepting the hypothesized structural equation model, this study found that information, system, and service quality were influenced by the mediating effect of perceived ease of use and the perceived usefulness of the technology acceptance model. The use of telehealth care systems among chronic patients increased significantly.

IMPACT OF FERTILISER APPLICATION ON BASAL STEM ROT AND LOWER FROND DESICCATION IN OIL PALMS ESTABLISHED IN PEAT SOIL

For oil palms established in deep peat soils in the Labuhan Batu district, North Sumatra, Ganoderma basal stem rot (BSR) and lower frond desiccation (LFD) are the two main maladies that have a serious impact on their long-term sustainability. Past experiences have shown that solitary control measures are unlikely to be successful in managing these two maladies and a number of integrated control strategies would be a prerequisite for their management. As nutrition is an important factor influencing fungal disease infection in many crops, nutrient manipulation can be considered as a vital component of any integrated disease management (IDM) system. This paper summarises the impact of fertiliser application on the level of BSR and LFD infection in five fertiliser trials, established in the affected areas from 1996-2021. In both trials established on first and second generation oil palms, application of nitrogen (N) and phosphorus (P) elicited a significant and linear increase in BSR infection levels, the higher the dosage, the higher the BSR incidence. In contrast, potassium (K) inputs had the reverse effect and significantly reduced BSR infection in the first trial but not in the second. A significant negative additive interaction between N x P and P x K was also observed in both trials. Increasing the rates of N together with P or P with K, increased BSR infection, with the highest infection levels being recorded at the highest N2P2 and P2K2 rates evaluated. Unlike macronutrients, application of boron (B), copper (Cu) and zinc (Zn) micronutrient fertilisers did not have any significant impact on BSR incidence. A similar trend was recorded on LFD incidence in both immature and mature second generation oil palms. Application of N and P also elicited a significant and linear increase in LFD and a strong negative and additive interaction effect between N x P and P x K was also observed. As for BSR, impact of K fertiliser was inconsistent, producing no positive or negative effects in immature palms and only significantly reducing LFD levels in two out of the 6 years of evaluation in mature oil palms. Likewise, application of B, Cu and Zn micronutrient fertilisers also had no impact on LFD incidence. Although peat is reported to be low in silica, application of silica fertilisers, even up to a dosage of 6 kg per palm per year produced no positive benefits. The relationship between nutrients and BSR/LFD infection and the physiological and biochemical mechanisms involved, remains unclear. However, it is interesting to note, that in all three NPK trials, the lowest infection rates were consistently recorded in the zero fertiliser plots. A possible hypothesis is that long-term application and concentration of N and P fertilisers in the weeded palm circles (WPC) could have had a negative impact on beneficial soil microbes inhabiting the root rhizosphere and associated with oil palm root protection or inducing systemic resistance in the oil palm. However, more detailed studies are required to ascertain the complex relationship between nutrients, soil microbes and disease infection. Keywords: Basal stem rot, fertilisers, lower frond desiccation, oil palm, peat.

GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and unmanned aerial system (UAS) imagery to track the spread of diseases, identify diseased regions within cornfields, and notify users with actionable information remains a research gap. Therefore, in this study, high-resolution, UAS-acquired, real-time kinematic (RTK) geotagged, RGB imagery at an altitude of 12 m above ground level (AGL) was used to develop the Geo Disease Location System (GeoDLS), a deep learning-based system for tracking diseased regions in corn fields. UAS images (resolution 8192 × 5460 pixels) were acquired in cornfields located at Purdue University’s Agronomy Center for Research and Education (ACRE), using a DJI Matrice 300 RTK UAS mounted with a 45-megapixel DJI Zenmuse P1 camera during corn stages V14 to R4. A dataset of 5076 images was created by splitting the UAS-acquired images using tile and simple linear iterative clustering (SLIC) segmentation. For tile segmentation, the images were split into tiles of sizes 250 × 250 pixels, 500 × 500 pixels, and 1000 × 1000 pixels, resulting in 1804, 1112, and 570 image tiles, respectively. For SLIC segmentation, 865 and 725 superpixel images were obtained using compactness (m) values of 5 and 10, respectively. Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. DenseNet169 identified diseased, healthy, and background regions with the highest testing accuracy of 100.00% when trained on images of tile size 1000 × 1000 pixels. Using a sliding window approach, the trained DenseNet169 model was then used to calculate the percentage of diseased regions present within each UAS image. Finally, the RTK geolocation information for each image was used to update users with the location of diseased regions with an accuracy of within 2 cm through a web application, a smartphone application, and email notifications. The GeoDLS could be a potential tool for an automated disease management system to track the spread of crop diseases, identify diseased regions, and provide actionable information to the users.

A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery

It is important to develop accurate disease management systems to identify and segment corn disease lesions and estimate their severity under complex field conditions. Although deep learning techniques are becoming increasingly popular to identify singular diseases, access to robust models for identifying multiple diseases and segmenting lesion areas for severity estimation under field conditions remain unsolved. In this study, a custom dataset consisting of handheld images of corn leaves infected with Gray Leaf Spot (GLS), Northern Leaf Blight (NLB), and Northern Leaf Spot (NLS) diseases, acquired under field conditions, was used to develop a novel two-stage semantic segmentation approach for identifying corn diseases and estimate their severity. Three semantic segmentation models were trained for each stage using SegNet, UNet, and DeepLabV3+ network architectures. Stage one used semantic segmentation to extract leaves from complex field backgrounds. In stage two, semantic segmentation was used to locate, identify, and calculate area coverage for disease lesions. After the models were trained, the best performance for stage one was observed from the UNet model, which achieved up to 0.9422 mean weighted intersection over union (mwIoU) and 0.8063 mean boundary F1-score (mBFScore). The best performance for stage two was observed from the DeepLabV3+ model, which could identify the disease lesions with a mwIoU of 0.7379 and mBFScore of 0.5351. Finally, severity was estimated by calculating the percentage of leaf area covered by disease lesions. In the test set, an R2 value (coefficient of determination) of 0.96 was achieved, which denotes that the integrated (UNet-DeepLabV3+) model predicted the severity of three diseases very close to the actual observations. This study developed a novel two-stage deep learning-based approach to accurately identify three targeted corn diseases and estimate their severity to pave the way for developing a field-worthy disease management system.

VI-RADS score system - A primer for urologists.

ABSTRACTBladder cancer (BCa) is one of the most common cancers worldwide and is also considered to be one of the most relapsing and aggressive neoplasms. About 30% of patients will present with muscle invasive disease, which is associated with a higher risk for metastatic disease. The aim of this article is to review the state of art imaging in Radiology, while providing a complete guide to urologists, with case examples, for the rationale of the development of the Vesical Imaging Reporting and Data System (VI-RADS), a scoring system emphasizing a standardized approach to multiparametric Magnetic Resonance Imaging (mpMRI) acquisition, interpretation, and reporting for BCa. Also, we examine relevant external validation studies and the consolidated literature of mpMRI for bladder cancer. In addition, this article discusses some of the potential clinical implications of this scoring system for disease management and follow-up.

Efficiency and Effectivity of a Biological–Epidemiological Fungal Disease Management System in Wheat—A Study of 26 Years

Foliar diseases are a major threat to worldwide wheat production, especially during the vegetative period in maritime climates. Despite advancements in agronomic practices, infestations by foliar diseases are possible under favourable weather conditions, thus, fungicides are essential for maintaining control. Stage-oriented applications are therefore common in farm practices. The optimization of fungicide use according to biological–epidemiological thresholds reduces the total amount of fungicides used, which is of political interest, especially in the European Union. Therefore, the efficiency and effectivity of the fungicides used to control the six major foliar diseases (Septoria tritici blotch, glume blotch, tans spot, powdery mildew, stripe rust, and leaf rust) were analysed in a long-term study of 26 years in northern Germany under favourable maritime conditions. Of those diseases, Septoria tritici blotch was the most dominant recurring disease, with high severity noted in every year of the study. The threshold-based disease management system was compared to a fungicide untreated control and a healthy-standard fungicide treatment (according to growth stages). The usage of the threshold-based system reduced the disease severities significantly compared to the fungicide untreated control, without any loss of yield compared to the healthy-standard fungicide treatment. Thereby, the use of fungicides was reduced by two thirds compared to the stage-oriented healthy-standard treatment. Thus, the advantages of the threshold-based system were obvious, and this approach will be an important tool for future evaluations of current farm practices.

Towards intelligent and integrated pest management through an AIoT-based monitoring system.

Main bottleneck in facilitating integrated pest management (IPM) is the unavailability of reliable and immediate crop damage data. Without sufficient insect pest and plant disease information, farm managers are unable to make proper decisions to prevent crop damage. This work aims to present how an integrated system was able to drive farm managers towards sustainable and data-driven IPM. A system called Intelligent and Integrated Pest and Disease Management (I2 PDM) system was developed. Edge computing devices were developed to automatically detect and recognize major greenhouse insect pests such as thrips (Frankliniella intonsa, Thrips hawaiiensis, and Thrips tabaci), and whiteflies (Bemisia argentifolii and Trialeurodes vaporariorum), to name a few, and measure environmental conditions including temperature, humidity, and light intensity, and send data to a remote server. The system has been installed in greenhouses producing tomatoes and orchids for gathering long-term spatiotemporal insect pest count and environmental data, for as long as 1368 days. The findings demonstrated that the proposed system supported the farm managers in performing IPM-related tasks. Significant yearly reductions in insect pest count as high as 50.7% were observed on the farms. It was concluded that novel and efficient strategies can be achieved by using an intelligent IPM system, opening IPM to potential benefits that cannot be easily realized with a traditional IPM program. This is the first work that reports the development of an intelligent strategic model for IPM based on actual automatically collected long-term data. The work presented herein can help in encouraging farm managers, researchers, experts, and industries to work together in implementing sustainable and data-driven IPM. © 2022 Society of Chemical Industry.

Care and Disease Management: Why Managed Care Organizations (MCOs) Need to have an Inclusive Approach to Patient Care

The health sector increasingly uses the terms "managed care" and "disease management," but few people understand them. Managed care refers to any health care system that pays for and provides treatment. Pressure to keep prices low has helped it flourish in the United States, and improved resource management is lowering costs. US managed care plans vary, but they share several traits. These include restricted doctor providers, secondary care access, selective contracting, financial incentives, quality monitoring, and utilization management. The NHS has all of these aspects, making it a united managed care system. Disease management coordinates resources across the health care system to treat patients throughout their illness. Business organizations have noticed that the NHS lacks a well-established system for disease management. Concerns regarding commercial engagement include the separation of basic medical services, the possibility of becoming a for-profit corporation, and the use of patient-based data. New guidelines may allow NHS disease control to increase.