Introduction to LitmusDx
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A short help presentation on how to use LitmusDx, an intelligent e-prescription software for the 21st century doctor.
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This project used Gradient Boosting to recognize physicians who have much higher prescription drug costs compared to their predicted cost. The Coefficient of Determination using this algorithm was .664. Additionally the accuracy of the model is such that 79.5% of physicians were no more than 40% above their predicted drug costs, while 88.2% were no more than 60% above their predicted costs.
What Is E-Prescribing?
E-prescribing allows doctors to electronically transmit new prescriptions and refill requests to a patient's pharmacy of choice. The process involves an electronic medical record system integrated with an e-prescribing technology provider that serves as a clearinghouse, transmitting prescriptions and refill requests between doctors, pharmacies, and benefits providers. This streamlines prescribing and refilling for both doctors and patients compared to traditional paper prescriptions.
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Ahmed Ezz is a mechanical power engineer seeking a new engineering position. He has a B.Sc. in Mechanical Engineering from Zagazig University in Egypt and over 6 years of experience as a site engineer and mechanical site manager in Egypt and Saudi Arabia. His experience includes the erection, fabrication, commissioning, and maintenance of various mechanical systems for water treatment plants, pumping stations, and other industrial facilities. He has strong computer and software skills.
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E-prescribing allows doctors to electronically transmit new prescriptions and refill requests to a patient's pharmacy of choice. The process involves an electronic medical record system integrated with an e-prescribing technology provider that serves as a clearinghouse, transmitting prescriptions and refill requests between doctors, pharmacies, and benefits providers. This streamlines prescribing and refilling for both doctors and patients compared to traditional paper prescriptions.
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This document discusses patient safety and medical errors. It notes that while human error is inevitable in healthcare, medical errors result in significant deaths and costs each year. To improve patient safety, the document advocates evaluating health systems, promoting a culture of reporting errors without blame, learning from mistakes, and designing systems to prevent errors and mitigate their effects if they do occur. The use of electronic health records and other technologies can help monitor patients and avoid some errors, but overreliance on computers also risks new types of mistakes, so limitations must be considered. Overall patient safety is improved when healthcare systems focus on evaluation, open communication, and making changes based on lessons learned over time.Application Development for Medication Tracker.pdf
Research shows that medication non-adherence accounts for around 100 billion avoidable medical expenses and 125,000 deaths annually. In addition, patients with chronic conditions such as diabetes or hypertension will struggle to remember to take their medication.
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This is an Android-based application in which an automatic alarm ringing system is implemented. It
focuses on doctor and patient interaction. Patients need not remember their medicine dosage timings as
they can set an alarm on their dosage timings. The alarm can be set for multiple medicines and timings
including date, time and medicine description. A notification will be sent to them through email or message
inside the system preferably chosen by the patients. They can search doctor disease wise. The patients will
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This could be a tiresome and time-consuming process if all you use is a notebook or notes on the phone. Such notes can get easily lost, and they don’t really help you keep track of everything you need to write down. This is why a new type of apps appeared - chronic condition health apps. Such apps help users manage their chronic illnesses more easily.
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2) Customizable to fit your needs and lifestyle
3) Manage your health regimen, status, and history
4) Track, monitor and view your health trends
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Our work as consultants primarily involve implementing CRM systems to consolidate clinical and administrative data from EHRs and health plans for patient care coordination, medical tourism, transitional care, aftercare and case management. In the case of a hospital setting, they are using Mckesson Paragon EHR using ICD 10, CPT and LOINC to capture data associated to problem lists, medical history, procedures, medical orders, and test results. In the case of medications, they are using RxNorm. The system can handle SNOMED but they are only using ICD. In the case of the health plan, the data we gather is based on ICD, CPT, and NDC only. In another project, we are working to establish a centralized system to capture all test results of Puerto Rico for abnormalities identification, patient and provider notification. In addition, this data will be used to analyze health population the data we are receiving include terminology type, LOINC or CPT. Depending on the laboratory information system vendor we get the CPT or LOINC code.
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Current data and analytics workflow tools are making ADE surveillance, monitoring, and prevention increasingly more effective with four key capabilities:
Perspective surveillance for ADEs and identification of previously undescribed ADEs.
Identification of the root cause of many ADEs by drug class.
Prescription at appropriate doses for patients with compromised kidney or liver functions.
Identification of different types of harm to find causes.
DESIGN OF A SAFE AND SMART MEDICINE BOX
This paperpresents an advanced medicine intake monitoring and control system. The proposed system consists on a smart medicine box that is designed to help patients taking their medications on time. Two main features are implemented during the design phase of this system: the first one is the safety feature that will alarm the patient in case of not taking and/or bad dosage taking for medication, will keep the medication out of reach of the children and will be used in case of failure of one of the system’s components whereas the second feature is the low cost of the system. This box will be linked to a phone
application that can signal several alarms mainly when the medications are about to finish, the patient forgets to take his medication or he (she) doesn’t take the required dose (or the required number of pills)… This system was tested over 30 different medications and when using ten different mobile phones by more than 25 patients and the results didn’t show any faulty alarms.
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interfaces. In addition to these applications it is aimed to implement a new technique which extracts the text-based
symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other
indicators. This study is applicable for patient`s everyday language statements besides medical expression of
symptoms for corresponding symptoms which is also supporting initial clinical decision system. Extracted terms
are analyzed for matching diagnosis where an accuracy of 90% has been accomplished.
Text Extraction Engine to Upgrade Clinical Decision Support System
New generation technological improvements lead patients to search their symptoms and
corresponding diagnosis on online resources. In this study, it is aimed to develop a machine learning model to
suit in different availability of users. Most of the current systems allow people to choose related symptom in web
interfaces. In addition to these applications it is aimed to implement a new technique which extracts the text-based
symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other
indicators. This study is applicable for patient`s everyday language statements besides medical expression of
symptoms for corresponding symptoms which is also supporting initial clinical decision system. Extracted terms
are analyzed for matching diagnosis where an accuracy of 90% has been accomplished.
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The document outlines several strategies to reduce medication errors, including increasing awareness of high-risk populations like pediatrics and geriatrics, avoiding abbreviations, recognizing look-alike and sound-alike medications, focusing on high-alert medications, reporting errors to improve processes, controlling the environment, and educating patients. It emphasizes the importance of a culture of safety, team approach, best practices, and technical support strategies to continuously improve patient safety.
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This whitepaper discusses about a glucose meter embedded in smartphones for monitoring the blood glucose level of the user, where it acts as a user-friendly device for the patient. SEGD (Smartphone embedded glucose device) represents the patient’s real-time glucose monitoring system. The user takes a glucose test through the smartphone using test strips and the result of the glucose measurement is displayed with the corresponding data. The generated BGM in the SEGD device shall dynamically be sent to the doctor’s phone and doctor can send back the necessary prescriptions after reviewing the complete history of that patient.
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including date, time and medicine description. A notification will be sent to them through email or message
inside the system preferably chosen by the patients. They can search doctor disease wise. The patients will
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Website : https://bit.ly/33Fwsye
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Whatsapp: +91 9884350006
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1) One stop shop for your personalized health records
2) Customizable to fit your needs and lifestyle
3) Manage your health regimen, status, and history
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Our work as consultants primarily involve implementing CRM systems to consolidate clinical and administrative data from EHRs and health plans for patient care coordination, medical tourism, transitional care, aftercare and case management. In the case of a hospital setting, they are using Mckesson Paragon EHR using ICD 10, CPT and LOINC to capture data associated to problem lists, medical history, procedures, medical orders, and test results. In the case of medications, they are using RxNorm. The system can handle SNOMED but they are only using ICD. In the case of the health plan, the data we gather is based on ICD, CPT, and NDC only. In another project, we are working to establish a centralized system to capture all test results of Puerto Rico for abnormalities identification, patient and provider notification. In addition, this data will be used to analyze health population the data we are receiving include terminology type, LOINC or CPT. Depending on the laboratory information system vendor we get the CPT or LOINC code.
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Current data and analytics workflow tools are making ADE surveillance, monitoring, and prevention increasingly more effective with four key capabilities:
Perspective surveillance for ADEs and identification of previously undescribed ADEs.
Identification of the root cause of many ADEs by drug class.
Prescription at appropriate doses for patients with compromised kidney or liver functions.
Identification of different types of harm to find causes.
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This paperpresents an advanced medicine intake monitoring and control system. The proposed system consists on a smart medicine box that is designed to help patients taking their medications on time. Two main features are implemented during the design phase of this system: the first one is the safety feature that will alarm the patient in case of not taking and/or bad dosage taking for medication, will keep the medication out of reach of the children and will be used in case of failure of one of the system’s components whereas the second feature is the low cost of the system. This box will be linked to a phone
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Text Extraction Engine to Upgrade Clinical Decision Support System
New generation technological improvements lead patients to search their symptoms and
corresponding diagnosis on online resources. In this study, it is aimed to develop a machine learning model to
suit in different availability of users. Most of the current systems allow people to choose related symptom in web
interfaces. In addition to these applications it is aimed to implement a new technique which extracts the text-based
symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other
indicators. This study is applicable for patient`s everyday language statements besides medical expression of
symptoms for corresponding symptoms which is also supporting initial clinical decision system. Extracted terms
are analyzed for matching diagnosis where an accuracy of 90% has been accomplished.
Text Extraction Engine to Upgrade Clinical Decision Support System
New generation technological improvements lead patients to search their symptoms and
corresponding diagnosis on online resources. In this study, it is aimed to develop a machine learning model to
suit in different availability of users. Most of the current systems allow people to choose related symptom in web
interfaces. In addition to these applications it is aimed to implement a new technique which extracts the text-based
symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other
indicators. This study is applicable for patient`s everyday language statements besides medical expression of
symptoms for corresponding symptoms which is also supporting initial clinical decision system. Extracted terms
are analyzed for matching diagnosis where an accuracy of 90% has been accomplished.
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Cell Therapy Expansion and Challenges in Autoimmune Disease
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
A Classical Text Review on Basavarajeeyam
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Journal Article Review on Rasamanikya
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Introduction to LitmusDx
- 1. LitmusDx website: www.litmusdx.com Watch videos about the design philosophy behind India’s first clinical decision support system. Sign up for a free trial of LitmusDx beta version, complete with cloudbased Patient EMRs.
- 4. Signing In: http://trial.litmusdx.com Users can choose to sign in with either their Google or Yahoo ID, or set up an account directly on LitmusDx. Short video summarizes how the system works.
- 5. Landing Screen One-level menu for easy navigation. Repeat patients can be added back, new patient records can be created. All patient consultations are consolidated to form EMRs. Account settings allow for tailored prescriptions.
- 6. Account Settings Prescriptions headers and doctor contact information fields are fully customizable. Built-in preview allows users to check look-and-feel quickly
- 7. Symptoms, Signs & Risk Factors Patient header details displayed. Vitals such as temperature and BP can be directly entered, and is taken as an input to the decision-support algorithm. Symptoms & signs can be entered in one block, with duration and severity as optional fields. Three symptoms help the system generate meaningful differential diagnosis.
- 8. Disease Selection Symptoms & signs are presented in a compact manner in the first part of the pane. Once diseases are selected (or directly entered), the appropriate STPs and Tests (investigations) can be reviewed on demand.
- 9. Differential Diagnosis Differential diagnosis popup has two elements – the first lists more symptoms that can be selected to increase diagnostic accuracy. The second part of the popup is the a list of 10 most probable diseases with associated symptoms.
- 10. Standard Treatment Protocol Specific conditions and the nature in which the disease presents are detailed within each Standard Treatment Protocol.
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- 12. Prescription Auto-fill Drug dosages and frequency of use are automatically calculated based on patient’s age, weight and gender.
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- 14. Prescription-related Precautions Drug-drug interactions typically take place between drugs that are part of different treatment protocols. Contraindications for use are those conditions prevent that use of the drugs. Pregnancy is a typical example. Adverse Drug Reactions, separately displayed by severity, can occur in a small percentage of patients using the drugs prescribed.
- 15. Prescription Preview Final prescription preview available, ready for print, and is emailed to patient as well.
- 16. Prescriptions stored as EMR Each prescription tagged by date, most recent first.