Tag Archives: used


Telehealth is increasingly becoming an integral part of post-acute care in various settings such as skilled nursing facilities, inpatient rehabilitation facilities, long-term acute care hospitals, and in the home health and hospice care settings. As healthcare moves more towards value-based models that focus on quality outcomes and keeping patients healthy at home whenever possible, telehealth provides opportunities to enhance care coordination, improve access to specialty providers, and reduce readmissions from post-acute care settings back to hospitals. Some of the key ways telehealth is being used in post-acute care include:

Remote Patient Monitoring: Many post-acute care patients, especially those with chronic conditions, can benefit from ongoing remote monitoring of vital signs and symptoms at home. Conditions like congestive heart failure, chronic obstructive pulmonary disease (COPD), diabetes and wound care are well-suited for remote monitoring. Devices can track things like blood pressure, heart rate, oxygen saturation, weight, and glucose levels and transmit the data via Bluetooth or Wi-Fi to the patient’s clinicians for review. This allows earlier detection of potential issues before they worsen and require a readmission. It also empowers patients to better self-manage their conditions at home with oversight from their care team.

Video Conferencing Visits: Secure video conferencing provides a way for clinicians to remotely “visit” with their post-acute patients to assess their conditions, answer questions and ensure treatment plans are on track for recovery and health maintenance. This is useful for providers to conduct virtual follow-up visits for things like wound care, medication management and therapy progress without requiring an in-person trip back to the facility or specialists’ offices. Therapy telehealth visits allow physical, occupational and speech therapists to remotely guide patients through exercises and provide training.

Specialty Consultations: Accessing specialty provider expertise can sometimes be challenging for post-acute facilities located in rural areas. Telehealth enables on-demand access to cardiologists, dermatologists, neurologists and others to evaluate patients as needed. Specialists can remotely examine patients, diagnose issues, adjust treatment plans and recommend additional testing or interventions to the bedside clinicians. This reduces transfers to hospitals or delays in advanced care. Tele-stroke programs similarly allow rapid neurology evaluations for acute stroke patients in remote facilities.

Discharge Planning & Care Transitions: Care coordinators use video visits to remotely prepare patients and families for discharge to lower levels of care or home. This could involve medication teaching, home safety evaluations, therapy scheduling and answering questions. Post-discharge remote follow ups via telehealth then allow earlier identification of any difficulties and opportunities for intervention to prevent readmissions. Virtual hospital rounding programs also utilize telehealth to better coordinate care as patients transition between acute and post-acute levels of care.

Staff Support & Education: Telehealth provides opportunities for off-site specialists, supervisors and educators to remotely support staff in post-acute facilities. Examples include consultations on complex patients, supervision and feedback on therapy techniques or wound care practices, teaching sessions on new policies/procedures and virtual observation of patient interactions to ensure quality and regulatory compliance. This enhances skills and knowledge while reducing travel time away from patient care duties.

Facility Usage Examples: Some real world examples of telehealth integration in post-acute care include:

A 200-bed skilled nursing facility in New York developed a comprehensive remote patient monitoring program utilizing Bluetooth-enabled devices. It reduced 30-day readmissions by 23% and led to earlier interventions for potential issues.

An inpatient rehabilitation hospital in Texas conducted over 7,500 video therapy and specialty telehealth visits in 2020, allowing continued treatment during the pandemic’s visiting restrictions while avoiding unnecessary transfers.

A home health agency partnered with a major hospital system to launch virtual hospital-at-home programs using remote patient monitoring. Initial data showed readmissions were 57% lower than similar in-patients.

A long-term acute care hospital collaborated with neurologists at a large medical center to run a tele-stroke program. Over 90% of patients received a same-day remote neurology evaluation and management plan compared to average 2 day wait previously.

As policymakers and payers increasingly recognize telehealth’s benefits, its role in post-acute care coordination and disease management will likely expand further in the coming years. Outcomes data thus far indicates telehealth technology can reduce costs while maintaining or improving quality of care and patient/family satisfaction during recovery and transition periods. With clinicians facing workforce shortages as well, telehealth ensures geography is not a barrier to accessing specialists and continued recovery support.


Excel provides users with a large canvas to organize, analyze, and share data using rows and columns in an intuitive grid format. Being able to view information in a tabular format allows users to easily input, calculate, filter, and sort data. The grid structure of Excel makes it simple for people to understand complex data sets and relationships at a glance. This ability to represent vast amounts of data visually and interpret patterns in an efficient manner has contributed greatly to Excel’s utility.

Beyond just viewing and inputting data, Excel’s built-in formulas and functions give users powerful tools to manipulate and derive insights from their information. There are over 400 functions available in Excel covering categories like financial, logical, text, date/time, math/trigonometry, statistical and more. Users can quickly perform calculations, lookups, conditional logic and other analytics that would be tedious to do manually. Excel essentially automates repetitive and complex computations, allowing knowledge workers and analysts to focus more on analysis rather than data wrangling. Some of the most commonly used formulas include SUM, AVERAGE, IF, VLOOKUP and more which many consider indispensable.

In addition to formulas and functions, Excel offers users control and flexibility through features like pivot tables, charts, filtering, conditional formatting and macros. Pivot tables allow users to easily summarize and rearrange large data sets to gain different perspectives. Charts visually represent data through over 50 different chart types including line graphs, pie charts, bar charts and more. Filtering and conditional formatting options enable users to rapidly identify patterns, outliers and focus on the most important subsets of data. Macros give power users the ability to record and automate repetitive tasks. These visualization, analysis and customization tools have made Excel highly customizable for a wide range of use cases across industries.

Excel also enables powerful collaboration capabilities through features like shared workbooks, comments, track changes and its integration with Microsoft 365 apps. Multiple users can work on the same file simultaneously with automatic merging of changes. In-cell comments and tracked changes allow for review and discussion of work without disrupting the original data. And Excel seamlessly integrates with the broader Office 365 suite for additional collaboration perks like co-authoring, shared online storage and integrated communication tools. This has allowed Excel to become the backbone of collaborative work and data management in many organizational departments and project teams.

From a technical perspective, Excel stores information using a proprietary binary file format with theXLS and XLSX extensions that allows for very large file sizes of up to 1 million rows by 16,000 columns. It can manage immense datasets far exceeding what other programs like conventional databases can handle. This capability combined with processing power optimizations has enabled Excel to perform complex analytics on huge data volumes. The software is highly customizable through its extensive macro programming capability using Visual Basic for Applications(VBA). Advanced users have leveraged VBA for automating entire workflows and building specialized Excel applications.

In terms of platform availability, Excel is broadly compatible across Windows, macOS, iOS and web browsers through Microsoft 365 web apps. This wide cross-platform reach allows Excel files to be easily shared, accessed and edited from anywhere using many different devices. The software also integrates tightly with other Windows and Microsoft services and platforms. For businesses already entrenched in the Microsoft ecosystem, Excel has proven to be an indispensable part of their technology stack.

Finally, Excel has earned mindshare and market dominance through its massive library of educational materials, third-party tools and large community online. Courses, tutorials, books and certifications help both beginners and experts continually expand their Excel skillsets. A vast ecosystem of add-ins, templates and specialized software partners further extend Excel’s capabilities. Communities on sites like MrExcel.com provide forums for collaboration and knowledge exchange among Excel power users worldwide. This network effect has solidified Excel’s position as a universal language of business and data.

Excel’s intuitive user interface, powerful built-in tools, high data capacity, extensive customization options, collaboration features, cross-platform availability, integration capabilities, large community and decades of continuous product refinement have made it the spreadsheet solution of choice for organizations globally. It remains the most widely deployed platform for organizing, analyzing, reporting and sharing data across all sizes of business, government and education. This unmatched combination of usability and functionality is what cements Excel as one of the most essential software programs in existence today.


Artificial intelligence has the potential to significantly improve and optimize transportation logistics systems. AI applications that leverage machine learning, predictive analytics, and optimization algorithms can help address many of the complex challenges involved in planning and executing efficient transportation of goods and people. Some key ways that AI is already enhancing transportation logistics include:

Route Optimization: Transportation networks involve routing vehicles between numerous pickup and delivery locations subject to timing constraints and other requirements. AI route optimization systems use algorithms to analyze huge amounts of historical and real-time data on locations, demand patterns, traffic conditions, and vehicle attributes to continuously generate the most efficient route plans. This helps maximize fleet utilization, reduce mileage and fuel costs, balance workloads, and better meet service-level commitments. For example, large package delivery companies use AI to optimize daily routes for tens of thousands of drivers based on predicted package volumes and dynamic traffic updates.

Demand Forecasting: Accurately anticipating transportation demand patterns is crucial for procurement, capacity planning, and resource allocation decisions across industries like freight, ride-hailing, public transit, and more. AI-powered demand forecasting models apply time series analysis, neural networks, and other machine learning techniques to historical usage and external indicator data to generate highly accurate short and long-term demand projections. These enable optimization of pricing, fleet sizing, facility locations, inventory levels and more based on predicted needs.

Supply Chain Visibility: Effective transportation management requires end-to-end visibility into inventory levels, orders, fleet locations, and other aspects of complex supply chain networks. AI is enhancing visibility through technologies like computer vision, geospatial analytics, and sensor data fusion. For example, object detection algorithms applied to images and videos from cameras in warehouses, trucks and drones help provide real-time insights into inventory levels, activities at distribution centers, traffic conditions impacting transit times and more.

Predictive Maintenance: Downtime for maintenance and repairs greatly impacts transportation efficiency and costs. AI is helping to maximize vehicle and equipment uptime through predictive maintenance approaches. Machine learning models analyze operational data streams from sensors embedded in vehicles, infrastructure and other assets to detect anomalies indicating pending equipment failures or performance issues. This enables proactive repairs and parts replacements to be scheduled before breakdowns occur.

Dynamic Routing: Real-time AI-powered routing optimization is enhancing dynamic ride-hailing, same-day delivery, and other transportation services where routes must adapt rapidly based on constantly changing conditions. Machine learning algorithms process live traffic, order, and vehicle location updates to dynamically reroute drivers as needed to optimize new pickups, avoid congestion and reduce idle time between trips. This helps maximize revenue per vehicle and service levels.

Automated Processes: AI is automating previously manual transportation and logistics tasks to reduce costs and free up human workers for more strategic roles. Examples include using computer vision for automated load tracking, natural language processing for chatbots to answer customer questions, and robotics for autonomous material handling equipment in warehouses. AI is also powering the automation of complex multi-step transportation management functions like dispatching, order consolidation, real-time capacity adjustments and more.

Autonomous Vehicles: Longer term, autonomous vehicle technologies enabled by AI will revolutionize transportation logistics. Self-driving trucks, delivery drones and robotaxis will allow goods and people to be transported more safely and efficiently with optimized routing and platooning. Autonomy will reduce labor costs while increasing vehicle utilization rates. It also enables new on-demand mobility services and just-in-time logistics approaches reliant on autonomous last-mile delivery. While large-scale implementation of autonomous logistics fleets faces technical and regulatory challenges, AI-powered vehicles are already enhancing functions like highway piloting, depot operations and dynamic routing.

Machine learning algorithms, predictive models, computer vision systems, natural language interfaces and other AI technologies are unlocking new possibilities for logistics optimization across industries and modes of transportation. Challenges remain around data quality, scalability, integration complexity, and developing human-AI collaboration best practices. As transportation companies continue investing in AI-driven solutions and building expertise in applying these technologies, the potential for transportation logistics transformation and efficiency gains is immense. AI will be a core driver of the future of intelligent transportation systems and smart supply chain management. With further advances, AI-powered logistics may one day approach the optimal efficiency of theoretical planning models while maintaining required levels of resilience, adaptability and safety.


Deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have shown strong performance for sentiment analysis of text data. Deep learning models are capable of automatically learning representations of text needed for sentiment classification from large amounts of unlabeled training data through architectures inspired by the human brain.

CNNs have proven effective for sentiment analysis because their sliding window approach allows them to identify sentiment-bearing n-grams in text. CNNs apply consecutive layers of convolutions and pooling operations over word embeddings or character n-grams to extract key features. The final fully connected layers then use these features for sentiment classification. A CNN can learn effective n-gram features in an end-to-end fashion without needing feature engineering.

RNNs, particularly long short-term memory (LSTM) and gated recurrent unit (GRU) networks, are well-suited for sentiment analysis due to their ability to model contextual information and long distance relationships in sequential data like sentences and documents. RNNs read the input text sequentially one token at a time and maintain an internal state to capture dependencies between tokens. This makes them effective at detecting sentiment that arises from longer-range contextual cues. Bidirectional RNNs that process the text in both the forward and backward directions have further improved results.

CNN-RNN hybrid models that combine the strengths of CNNs and RNNs have become very popular for sentiment analysis. In these models, CNNs are applied first to learn n-gram features from the input embeddings or character sequences. RNN layers are then utilized on top of the CNN layers to identify sentiment based on sequential relationships between the extracted n-gram features. Such models have achieved state-of-the-art results on many sentiment analysis benchmarks.

Rule-based techniques such as dictionary-based approaches are also used for sentiment analysis. Dictionary-based techniques identify sentiment words, phrases and expressions in the text by comparing them against predefined sentiment dictionaries or lexicons. Scoring is then performed based on the sentiment orientation and strength of the identified terms. While not as accurate as machine learning methods due to their dependence on the completeness of dictionaries, rule-based techniques still see use for simplicity and interpretability. They can also supplement ML models.

Aspect-based sentiment analysis techniques aim to determine sentiment at a more granular level – towards specific aspects, features or attributes of an entity or topic rather than the overall sentiment. They first identify these aspects from text, map sentiment-bearing expressions to identified aspects, and determine polarity and strength of sentiment for each aspect. Techniques such as rule-based methods, topic modeling, and supervised ML algorithms like SVMs or deep learning have been applied for aspect extraction and sentiment classification.

Unsupervised machine learning techniques can also be utilized to some extent for sentiment analysis when labeled training data is limited. In these techniques, machine learning models are trained without supervision by only utilizing unlabeled sentiment data. Examples include clustering algorithms like k-means clustering to group messages into positive and negative clusters based on word distributions and frequencies. Dimensionality reduction techniques like principal component analysis (PCA) can also be applied as a preprocessing step to project text into lower dimensional spaces better suited for unsupervised learning.

In addition to the above modeling techniques, many advanced natural language processing and deep learning principles have been leveraged to further improve sentiment analysis results. Some examples include:

Word embeddings: Representing words as dense, low-dimensional and real-valued vectors which preserve semantic and syntactic relationships. Popular techniques include Word2vec, GloVe and FastText.

Attention mechanisms: Helping models focus on sentiment-bearing parts of the text by weighting token representations based on relevance to the classification task.

Transfer learning: Using large pretrained language models like BERT, XLNet, RoBERTa that have been trained on massive unlabeled corpora to extract universal features and initialize weights for downstream sentiment analysis tasks.

Data augmentation: Creating additional synthetic training samples through simple techniques like synonym replacement to improve robustness of models.

Multi-task learning: Jointly training models on related NLP tasks like topic modeling, relation extraction, aspect extraction to leverage shared representations and improve sentiment analysis performance.

Ensemble methods: Combining predictions from multiple models like SVM, CNN, RNN through averaging or weighted voting to yield more robust and accurate sentiment predictions than individual models.

While techniques like naïve Bayes and support vector machines formed the basis, latest deep learning and NLP advancements have significantly improved sentiment analysis. Hybrid models leveraging strengths of different techniques tend to work best in practice for analyzing customer feedback at scale in terms of both accuracy and interpretability of results.


Genetic engineering promises revolutionary medical advances but also raises serious ethical concerns if not adequately regulated. Ensuring its responsible and ethical development and application will require a multifaceted approach with oversight and participation from government, scientific institutions, and the general public.

Government regulation provides the foundation. Laws and regulatory agencies help define ethical boundaries, require safety testing, and provide oversight. Regulation should be based on input from independent expert committees representing fields like science, ethics, law, and public policy. Committees can help identify issues, provide guidance to lawmakers, and review proposed applications. Regulations must balance potential benefits with risks of physical or psychological harms, effects on human dignity and identity, and implications for societal equality and justice. Periodic review is needed as technologies advance.

Scientific institutions like universities also have an important responsibility. Institutional review boards can evaluate proposed genetic engineering research for ethical and safety issues before approval. Journals should require researchers to disclose funding sources and potential conflicts of interest. Institutions must foster a culture of responsible conduct where concerns can be raised without fear of reprisal. Peer review helps ensure methods and findings are valid, problems are identified, and results are communicated clearly and accurately.

Transparency from researchers is equally vital. Early and meaningful public engagement allows input that can strengthen oversight frameworks and build trust. Researchers should clearly explain purposes, methods, funding, uncertainties, and oversight in language the non-expert public can understand. Public availability of findings through open-access publishing or other means supports informed debate. Engagement helps address concerns and find ethical solutions. If applications remain controversial, delaying or modifying rather than dismissing concerns shows respect.

Some argue results should only be applied if a societal consensus emerges through such engagement. This risks paralysis or domination by a minority view. Still, research approvals could require engagement plans and delay controversial applications if outstanding public concerns exist. Engagement allows applications most in need of discussion more time and avenues for input before proceeding. The goal is using public perspectives, not votes, to strengthen regulation and address public values.

Self-governance within the scientific community also complements external oversight. Professional codes of ethics outline boundaries for techniques like human embryo research, genetic enhancement, or editing heritable DNA. Societies like genetics associations establish voluntary guidelines members agree to follow regarding use of new techniques, clinical applications, safety testing, and oversight. Such codes have legitimacy when developed through open processes including multiple perspectives. Ethics training for researchers helps ensure understanding and compliance. Voluntary self-regulation gains credibility through transparency and meaningful consequences like loss of certification for non-compliance.

While oversight focuses properly on research, broader societal issues around equitable access must also be addressed. Prohibitions on genetic discrimination ensure no one faces disadvantage in areas like employment, insurance or education due to genetic traits. Universal healthcare helps ensure therapies are available based on need rather than ability to pay. These safeguards uphold principles of justice, human rights and social solidarity. Addressing unjust inequalities in areas like race, gender and disability supports ethical progress overall.

Societal discussion also rightly focuses on defining human identity, enhancement and our shared humanity. Reasonable views diverge and no consensus exists. Acknowledging these profound issues and inquiring respectfully across differences supports envisioning progress all can find ethical. Focusing first on agreed medical applications while continuing open yet constructive discussions models the democratic and compassionate spirit needed. Ultimately the shared goal should be using genetic knowledge responsibly and equitably for the benefit of all.

A multifaceted approach with expertise and participation from diverse perspectives offers the best framework for ensuring genetic engineering progresses ethically. No system will prevent all problems but this model balances oversight, transparency, inclusion, justice and ongoing learning—helping to build understanding and trust so society can begin to realize genetic advances’ promise while carefully addressing uncertainties and implications these new technologies inevitably raise. With open and informed democratic processes, guidelines that prioritize well-being and human dignity, and oversight that safeguards yet does not hinder, progress can proceed in a responsible manner respecting all.