Reporting interhospital neonatal intensive care transport: international five-step Delphi-based template.

OBJECTIVE: To develop a general and internationally applicable template of data variables for reporting interhospital neonatal intensive care transports. DESIGN: A five-step Delphi method. SETTING: A group of experts was guided through a formal consensus process using email. SUBJECTS: 12 experts in neonatal intensive care transports from Canada, Denmark, Norway, the UK and the USA. Four women and eight men. The experts were neonatologists, anaesthesiologists, intensive care nurse, anaesthetic nurse, medical leaders, researchers and a parent representative. MAIN OUTCOME MEASURES: 37 data variables were included in the final template. RESULTS: Consensus was achieved on a template of 37 data variables with definitions. 30 variables to be registered for each transport and 7 for annual registration of the system of the transport service. 11 data variables under the category structure, 20 under process and 6 under outcome. CONCLUSIONS: We developed a template with a set of data variables to be registered for neonatal intensive care transports. To register the same data will enable larger datasets and comparing services.

PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.

Introduction: PANDEM-Source (PS) is a tool to collect and integrate openly available public health-related data from heterogeneous data sources to support the surveillance of infectious diseases for pandemic management. The tool may also be used for pandemic preparedness by generating surveillance data for training purposes. It was developed as part of the EU-funded Horizon 2020 PANDEM-2 project during the COVID-19 pandemic as a result of close collaboration in a consortium of 19 partners, including six European public health agencies, one hospital, and three first responder organizations. This manuscript describes PS's features and design to disseminate its characteristics and capabilities to strengthen pandemic preparedness and response. Methods: A requirement-gathering process with EU pandemic managers in the consortium was performed to identify and prioritize a list of variables and indicators useful for surveillance and pandemic management. Using the COVID-19 pandemic as a use case, we developed PS with the purpose of feeding all necessary data to be displayed in the PANDEM-2 dashboard. Results: PS routinely monitors, collects, and standardizes data from open or restricted heterogeneous data sources (users can upload their own data). It supports indicators and health resources related data from traditional data sources reported by national and international agencies, and indicators from non-traditional data sources such as those captured in social and mass media, participatory surveillance, and seroprevalence studies. The tool can also calculate indicators and be used to produce data for training purposes by generating synthetic data from a minimal set of indicators to simulate pandemic scenarios. PS is currently set up for COVID-19 surveillance at the European level but can be adapted to other diseases or threats and regions. Conclusion: With the lessons learnt during the COVID-19 pandemic, it is important to keep building capacity to monitor potential threats and develop tools that can facilitate training in all the necessary aspects to manage future pandemics. PS is open source and its design provides flexibility to collect heterogeneous data from open data sources or to upload end users's own data and customize surveillance indicators. PS is easily adaptable to future threats or different training scenarios. All these features make PS a unique and valuable tool for pandemic management.

Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study.

BACKGROUND: Digitalizing oral health data through an app can help manage the extensive data obtained through oral health surveys. The Tooth Memo app collects data from oral health surveys and personal health information. OBJECTIVE: This study aims to evaluate the evaluate the time efficiency, reliability, and user satisfaction of the Tooth Memo app. METHODS: There are 2 sections in the Tooth Memo app: oral health survey and personal oral health record. For the oral health survey section of the Tooth Memo app, different data entry methods were compared and user satisfaction was evaluated. Fifth-year dental students had access to the oral health survey section in the Tooth Memo app during their clinical work. The time required for data entry, analysis, and summary of oral health survey data by 3 methods, that is, pen-and-paper (manual), Tooth Memo app on iOS device, and Tooth Memo app on Android device were compared among 3 data recorders who entered patients' information on decayed, missing, and filled permanent teeth (DMFT) index and community periodontal index (CPI), which were read aloud from the database of 103 patients by another dental personnel. The interobserver reliability of the 3 different data-entering procedures was evaluated by percent disagreement and kappa statistic values. Laypeople had access to the personal oral health record section of this app, and their satisfaction was evaluated through a Likert scale questionnaire. The satisfaction assessments for both sections of the Tooth Memo app involved the same set of questions on the app design, usage, and overall satisfaction. RESULTS: Of the 103 dental records on DMFT and CPI, 5.2% (177/3399) data points were missing in the manual data entries, but no data on tooth status were missing in the Android and iOS methods. Complete CPI information was provided by all 3 methods. Transferring data from paper to computer took an average of 55 seconds per case. The manual method required 182 minutes more than the iOS or Android methods to clean the missing data and transfer and analyze the tooth status data of 103 patients. The users, that is, 109 fifth-year dental students and 134 laypeople, expressed high satisfaction with using the Tooth Memo app. The overall satisfaction with the oral health survey ranged between 3 and 10, with an average (SD) of 7.86 (1.46). The overall satisfaction with the personal oral health record ranged between 4 and 10, with an average (SD) of 8.09 (1.28). CONCLUSIONS: The Tooth Memo app was more efficacious than manual data entry for collecting data of oral health surveys. Dental personnel as well as general users reported high satisfaction when using this app.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Usability of an Automated System for Real-Time Monitoring of Shared Decision-Making for Surgery: Mixed Methods Evaluation.

BACKGROUND: Improving shared decision-making (SDM) for patients has become a health policy priority in many countries. Achieving high-quality SDM is particularly important for approximately 313 million surgical treatment decisions patients make globally every year. Large-scale monitoring of surgical patients' experience of SDM in real time is needed to identify the failings of SDM before surgery is performed. We developed a novel approach to automating real-time data collection using an electronic measurement system to address this. Examining usability will facilitate its optimization and wider implementation to inform interventions aimed at improving SDM. OBJECTIVE: This study examined the usability of an electronic real-time measurement system to monitor surgical patients' experience of SDM. We aimed to evaluate the metrics and indicators relevant to system effectiveness, system efficiency, and user satisfaction. METHODS: We performed a mixed methods usability evaluation using multiple participant cohorts. The measurement system was implemented in a large UK hospital to measure patients' experience of SDM electronically before surgery using 2 validated measures (CollaboRATE and SDM-Q-9). Quantitative data (collected between April 1 and December 31, 2021) provided measurement system metrics to assess system effectiveness and efficiency. We included adult patients booked for urgent and elective surgery across 7 specialties and excluded patients without the capacity to consent for medical procedures, those without access to an internet-enabled device, and those undergoing emergency or endoscopic procedures. Additional groups of service users (group 1: public members who had not engaged with the system; group 2: a subset of patients who completed the measurement system) completed user-testing sessions and semistructured interviews to assess system effectiveness and user satisfaction. We conducted quantitative data analysis using descriptive statistics and calculated the task completion rate and survey response rate (system effectiveness) as well as the task completion time, task efficiency, and relative efficiency (system efficiency). Qualitative thematic analysis identified indicators of and barriers to good usability (user satisfaction). RESULTS: A total of 2254 completed surveys were returned to the measurement system. A total of 25 service users (group 1: n=9; group 2: n=16) participated in user-testing sessions and interviews. The task completion rate was high (169/171, 98.8%) and the survey response rate was good (2254/5794, 38.9%). The median task completion time was 3 (IQR 2-13) minutes, suggesting good system efficiency and effectiveness. The qualitative findings emphasized good user satisfaction. The identified themes suggested that the measurement system is acceptable, easy to use, and easy to access. Service users identified potential barriers and solutions to acceptability and ease of access. CONCLUSIONS: A mixed methods evaluation of an electronic measurement system for automated, real-time monitoring of patients' experience of SDM showed that usability among patients was high. Future pilot work will optimize the system for wider implementation to ultimately inform intervention development to improve SDM. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2023-079155.

Challenges to conducting research on oral health with older adults living in long-term care facilities.

BACKGROUND: The challenges to conducting oral health studies involving older people in long-term care facilities (LTCFs) must be debated. OBJECTIVE: This study aimed to investigate researchers' perceptions and experiences while conducting an epidemiological survey on oral health among older individuals residing in LTCFs. METHODS: A qualitative study was conducted involving six researchers who utilized field diaries to record their impressions during data collection through interviews (older individuals (or their proxies), caregivers, and LTCF coordinators) and oral examinations of the older people participants. Additionally, researchers responded to open-ended questions about their experiences. The collected material was subjected to content analysis by two researchers. RESULTS: The themes that emerged from the analysis were institutional context, aspects affecting the operationalization of the study, and data collection oriented by the clinical-functional profile of the older people. According to the researchers' perceptions, LTCF coordinators demonstrated concern for the study's benefits for older adults and the preservation of institutional routines during the research process. Caregivers emerged as vital sources of information, guiding researchers in navigating the challenges posed by the physical and mental complexities of the older people participants, necessitating empathy, sensitivity, and attentive listening from the researchers. The organization of materials and a streamlined data collection process proved essential for optimizing time efficiency and reducing stress for participants and researchers. CONCLUSION: The researchers recognized the important role played by LTCF coordinators and formal caregivers, underscoring the significance of empathetic methodologies and streamlined data collection processes in mitigating the challenges inherent to research conducted within LTCFs.

The Landsteiner lung cancer research platform (LALUCA) : Objectives, methodology and implementation of a real-world clinical lung cancer registry.

BACKGROUND: Lung cancer is a major health burden in Austria; however, limited real-world data exist on the diagnostic and treatment reality of lung cancer patients in Austria. The collection of high-quality data in a clinical setting is needed to gain insights into the real-world diagnostic and therapeutic management of lung cancer patients. METHODS: The Karl Landsteiner Institute for Lung Research and Pulmonary Oncology implemented the Landsteiner lung cancer research platform (LALUCA), recruiting unselected lung cancer patients from two high volume centers in Vienna. This article describes the objectives, design, methodology of the registry and the process of implementation. RESULTS: A multidisciplinary team of lung cancer specialists created a custom designed variable catalogue for the LALUCA platform consisting of 17 categories with 180 variables. Detailed information on clinical characteristics, diagnostic interventions, molecular pathology as well as curative and palliative treatment modalities are collected. During an implementation phase in 2020, the platform was optimized using the data of 50 patients. Since then a total of 1200 patients have been enrolled. Recruitment for the registry is ongoing with a recruitment rate of approximately 400 patients per year. CONCLUSION: The LALUCA registry is a web-based platform for the collection of real-world clinical data of lung cancer patients. Combining a large number of patients with a focus on gathering comprehensive data on diagnosis and treatment, the LALUCA registry provides a tool for investigation, evaluation, and improvement of the clinical management, survival and quality of care of Austrian lung cancer patients.

A Road Defect Detection System Using Smartphones.

We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications.

Understanding social needs screening and demographic data collection in primary care practices serving Maryland Medicare patients.

BACKGROUND: Health outcomes are strongly impacted by social determinants of health, including social risk factors and patient demographics, due to structural inequities and discrimination. Primary care is viewed as a potential medical setting to assess and address individual health-related social needs and to collect detailed patient demographics to assess and advance health equity, but limited literature evaluates such processes. METHODS: We conducted an analysis of cross-sectional survey data collected from n = 507 Maryland Primary Care Program (MDPCP) practices through Care Transformation Requirements (CTR) reporting in 2022. Descriptive statistics were used to summarize practice responses on social needs screening and demographic data collection. A stepwise regression analysis was conducted to determine factors predicting screening of all vs. a targeted subset of beneficiaries for unmet social needs. RESULTS: Almost all practices (99%) reported conducting some form of social needs screening and demographic data collection. Practices reported variation in what screening tools or demographic questions were employed, frequency of screening, and how information was used. More than 75% of practices reported prioritizing transportation, food insecurity, housing instability, financial resource strain, and social isolation. CONCLUSIONS: Within the MDPCP program there was widespread implementation of social needs screenings and demographic data collection. However, there was room for additional supports in addressing some challenging social needs and increasing detailed demographics. Further research is needed to understand any adjustments to clinical care in response to identified social needs or application of data for uses such as assessing progress towards health equity and the subsequent impact on clinical care and health outcomes.

A Novel Electronic Record System for Documentation and Efficient Workflow for Community Health Workers: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic added to the decades of evidence that public health institutions are routinely stretched beyond their capacity. Community health workers (CHWs) can be a crucial extension of public health resources to address health inequities, but systems to document CHW efforts are often fragmented and prone to unneeded redundancy, errors, and inefficiency. OBJECTIVE: We sought to develop a more efficient data collection system for recording the wide range of community-based efforts performed by CHWs. METHODS: The Communities Organizing to Promote Equity (COPE) project is an initiative to address health disparities across Kansas, in part, through the deployment of CHWs. Our team iteratively designed and refined the features of a novel data collection system for CHWs. Pilot tests with CHWs occurred over several months to ensure that the functionality supported their daily use. Following implementation of the database, procedures were set to sustain the collection of feedback from CHWs, community partners, and organizations with similar systems to continually modify the database to meet the needs of users. A continuous quality improvement process was conducted monthly to evaluate CHW performance; feedback was exchanged at team and individual levels regarding the continuous quality improvement results and opportunities for improvement. Further, a 15-item feedback survey was distributed to all 33 COPE CHWs and supervisors for assessing the feasibility of database features, accessibility, and overall satisfaction. RESULTS: At launch, the database had 60 active users in 20 counties. Documented client interactions begin with needs assessments (modified versions of the Arizona Self-sufficiency Matrix and PRAPARE [Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences]) and continue with the longitudinal tracking of progress toward goals. A user-specific automated alerts-based dashboard displays clients needing follow-up and upcoming events. The database contains over 55,000 documented encounters across more than 5079 clients. Available resources from over 2500 community organizations have been documented. Survey data indicated that 84% (27/32) of the respondents considered the overall navigation of the database as very easy. The majority of the respondents indicated they were overall very satisfied (14/32, 44%) or satisfied (15/32, 48%) with the database. Open-ended responses indicated the database features, documentation of community organizations and visual confirmation of consent form and data storage on a Health Insurance Portability and Accountability Act-compliant record system, improved client engagement, enrollment processes, and identification of resources. CONCLUSIONS: Our database extends beyond conventional electronic medical records and provides flexibility for ever-changing needs. The COPE database provides real-world data on CHW accomplishments, thereby improving the uniformity of data collection to enhance monitoring and evaluation. This database can serve as a model for community-based documentation systems and be adapted for use in other community settings.

Ambient air concentrations of plant protection products: Data collection for the combined air concentration database and associated risk assessment.

CropLife Europe collected literature values from monitoring studies measuring air concentrations of Plant Protection Products (PPPs) that may be inhaled by humans located in rural areas but not immediately adjacent to PPP applications. The resulting "Combined Air Concentration Database" (CACD) was used to determine whether air concentrations of PPPs reported by the French "Agency for Food, Environmental and Occupational Health & Safety" (ANSES) are consistent with those measured by others to increase confidence in values of exposure to humans. The results were put into risk assessment context. Results show that 25-90% of samples do not contain measurable PPP concentrations. Measured respirable fractions were below EU default air concentrations used for risk assessment for resident exposure by the European Food Safety Authority. All measured exposures in the CACD were also below established toxicological endpoints, even when considering the highest maximum average reported concentrations and very conservative inhalation rates. The highest recorded air concentration was for prosulfocarb (0.696 µg/m³ measured over 48 h) which is below the EFSA default limit of 1 µg/m³ for low volatility substances. In conclusion, based on the CACD, measured air concentrations of PPPs are significantly lower than EFSA default limits and relevant toxicological reference values.

Strategies to increase survey participation: a randomized controlled study in a population of breast cancer survivors.

BACKGROUND: Data collection by mailing questionnaires to the study population is one of the main research methods in epidemiologic studies. As participation rates are decreasing, easy-to-implement and cost-effective strategies to increase survey participation are needed. In this study, we tested the effect of a pragmatic combination of evidence-based interventions. METHODS: We conducted a two-armed randomized controlled trial, nested in a cohort of breast cancer survivors (n=1000) in the setting of a health outcomes survey. The intervention arm received a postal pre-notification, a non-monetary incentive (ballpoint with the study logo) and an alternative invitation letter in which several lay-out and textual adjustments were implemented according to behavioural science techniques. The alternative invitation letter also contained a QR-code through which an information video about the study could be accessed. The control arm was invited according to standard practice. Participants had the option to fill-out a questionnaire either on paper or online. A questionnaire with more than 50% of the questions answered classified as participation. RESULTS: Overall participation rate was 62.9%. No significant difference in participation rate was observed between intervention and control arm (64.5% vs 61.3%, Risk Ratio (RR) 1.05, 95% CI [0.96 - 1.16]). Older age at study (>65 vs <51 years), and high socio-economic status (highest vs lowest quartile) were associated with higher participation rates (RR 1.30, 95% CI [1.07 - 1.57] and 1.24, 95% CI [1.09 - 1.42] respectively). In-situ carcinoma compared to invasive cancer and longer interval since treatment were associated with lower participation (RR 0.86, 95% CI [0.74 - 0.99] and RR 0.92, 95% CI [0.87 - 0.99] per 5 year increase, respectively). CONCLUSION: Overall, the combination of four interventions tested in this study did not improve survey participation among breast cancer survivors. The overall participation rate was relatively high, possibly due to the study population of cancer survivors.

Determining Protein Structures Using X-Ray Crystallography.

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.

An examination of changes in psychological status across four phases of injury recovery and return to sport: A prospective weekly study of competitive athletes from acute care to return to sport.

While ample research has shown that sport injuries are associated with poor psychological status, scant attention has been given to changes in injured athletes' psychological status over the full course of recovery and return to sport. The aim of the present study was to prospectively investigate potential changes in injured athletes' psychological status across four phases of recovery and return to sport. A total of 38 severely injured adult competitive athletes (58% female; mean age 24.1 ± 7.18 years) participated in this prospective weekly investigation (n = 319 observations). Athletes' were asked to indicate their current phase of recovery or return to sport (acute care, rehabilitation, adapted training, or full return to sport) after which they responded to visual analog scales assessing post-injury psychological status, including: perceived pain (frequency and intensity), emotions (positive and negative), anxiety (cognitive and physiological), motivation, self-efficacy, and satisfaction. During the acute care phase, participants showed higher scores of perceived pain, and physiological anxiety compared to the other phases. During the adapted training phase, amotivation was higher than in the acute care phase, and self-efficacy was lower than in other phases. At full return to sport, athletes showed less perceived pain, cognitive anxiety, and more satisfaction than during other phases. The present study provides a deeper prospective understanding of changes in athlete's psychological status over the course of injury recovery and return to sport and highlight the importance of monitoring psychological status.

Justifying the need for a recovery related surveillance system: Exploratory focused interviews.

Background and Aims: No recovery related surveillance system exists but given the evidence of effectiveness and growing supply, a house- and resident- level recovery house (RH) surveillance system could be beneficial for data collection on recovery support service (RSS) engagement, and retention; for improved standardization of RH programs and services; and for identification of outcomes associated with long-term recovery. Methods: This study aimed to explore current data collection practices at the resident- and house- level through qualitative focus interviews of RH representatives. The 13 RH interviews were scheduled with 16 RH representative respondents. Results: The most frequently collected resident data was at entry (92%) and departure (85%) and included demographics (n = 5), substance use history (n = 6), treatment and recovery history (n = 5), legal and correctional history (n = 6) and mental health information (n = 7). Recovery support data was collected by 85% of houses. Post-stay data was only collected by four RHs (31%). Conclusion: These results indicate that there is a lack of standardized systematic collection, analysis, and reporting of recovery related data in the RH field. A recovery related surveillance system has the potential to fill this gap and inform and improve standard of resident care to support long-term recovery from substance use disorder.

Assessing the Usability and Feasibility of Digital Assistant Tools for Direct Support Professionals: Participatory Design and Pilot-Testing.

BACKGROUND: The United States is experiencing a direct support professional (DSP) crisis, with demand far exceeding supply. Although generating documentation is a critical responsibility, it is one of the most wearisome aspects of DSPs' jobs. Technology that enables DSPs to log informal time-stamped notes throughout their shift could help reduce the burden of end-of-shift documentation and increase job satisfaction, which in turn could improve the quality of life of the individuals with intellectual and developmental disabilities (IDDs) whom DSPs support. However, DSPs, with varied ages, levels of education, and comfort using technology, are not likely to adopt tools that detract from caregiving responsibilities or increase workload; therefore, technological tools for them must be relatively simple, extremely intuitive, and provide highly valued capabilities. OBJECTIVE: This paper describes the development and pilot-testing of a digital assistant tool (DAT) that enables DSPs to create informal notes throughout their shifts and use these notes to facilitate end-of-shift documentation. The purpose of the pilot study was to assess the usability and feasibility of the DAT. METHODS: The research team applied an established user-centered participatory design process to design, develop, and test the DAT prototypes between May 2020 and April 2023. Pilot-testing entailed having 14 DSPs who support adults with IDDs use the first full implementation of the DAT prototypes during 2 or 3 successive work shifts and fill out demographic and usability questionnaires. RESULTS: Participants used the DAT prototypes to create notes and help generate end-of-shift reports. The System Usability Scale score of 81.79 indicates that they found the prototypes easy to use. Survey responses imply that using the DAT made it easier for participants to produce required documentation and suggest that they would adopt the DAT if this tool were available for daily use. CONCLUSIONS: Simple technologies such as the DAT prototypes, which enable DSPs to use mobile devices to log time-stamped notes throughout their shift with minimal effort and use the notes to help write reports, have the potential to both reduce the burden associated with producing documentation and enhance the quality (level of detail and accuracy) of this documentation. This could help to increase job satisfaction and reduce turnover in DSPs, both of which would help improve the quality of life of the individuals with IDDs whom they support. The pilot test results indicate that DSPs found the DAT easy to use. Next steps include (1) producing more robust versions of the DAT with additional capabilities, such as storing data locally on mobile devices when Wi-Fi is not available; and (2) eliciting input from agency directors, families, and others who use data about adults with IDDs to help care for them to ensure that data produced by DSPs are relevant and useful.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.