Log In Sign up
The largest database of trusted experimental protocols
> Procedures > Health Care Activity > Prescriptions

Prescriptions

Prescriptions are written orders from a licensed healthcare provider to a pharmacist or other healthcare professional, specifying medication or treatment for a patient.
They play a crucial role in ensuring patients receive the appropriate drugs or therapies to manage their health conditions.
Prescriptions outline the drug name, dosage, frequency, and administration method, helping to promote safe and effective medication use.
Understanding the significance of prescriptions is essential for healthcare providers, pharmacists, and patients to optimize treatment outcomes and enhance patient safety.

Most cited protocols related to «Prescriptions»

1
Propensity Score Matching for Statin Exposure
The propensity score was estimated using logistic regression to regress receipt of a statin prescription at discharge on the 24 baseline covariates described in Table I. The estimated propensity score was the predicted probability of statin exposure derived from the fitted logistic regression model. In the propensity-score model we assumed a linear relationship between continuous covariates and the log-odds of receiving a statin prescription. Furthermore, the propensity-score model did not include any interactions.
We created a matched sample by matching treated and untreated subjects on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score [3 (link), 17 (link), 18 (link)]. A greedy, nearest-neighbour matching algorithm was employed to form pairs of treated and untreated subjects.
, & Austin P.C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083-3107.
Full text: Click here
Publication 2009
Hydroxymethylglutaryl-CoA Reductase Inhibitors Patient Discharge
2
Statin Prescription and Cardiovascular Outcomes
We used data on 9104 patients who were discharged alive following hospitalization with a diagnosis of acute myocardial infarction (AMI or heart attack) from 102 hospitals in Ontario, Canada, between April 1, 1999 and March 31, 2001. These data are similar to those reported on elsewhere [13 (link)–15 (link)], and were collected as part of the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) Study, an initiative focused on improving the quality of care for cardiovascular disease patients in Ontario [16 ]. Data on patient demographics, presenting signs and symptoms, classic cardiac risk factors, comorbid conditions and vascular history, vital signs on admission, and results of laboratory tests, were abstracted directly from patients’ medical records. The exposure of interest was whether the patient was prescribed a statin at hospital discharge. Overall, 3049 (33.5 per cent) of patients received a prescription for a statin at discharge, while 6055 (66.5 per cent) did not receive a prescription at discharge. Table I compares the means of continuous baseline covariates and prevalences of dichotomous baseline covariates between treated and untreated subjects in the original unmatched sample. The prevalence of dichotomous variables was compared between treated and untreated subjects using a Chi-squared test, while a standard two-sample t-test was used to compare continuous baseline covariates.
, & Austin P.C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083-3107.
Full text: Click here
Publication 2009
Blood Vessel Cardiovascular Diseases Diagnosis Heart Hospitalization Hydroxymethylglutaryl-CoA Reductase Inhibitors Myocardial Infarction Patient Discharge Patients Quality of Health Care Signs, Vital
3
Comprehensive Traditional Chinese Medicine Database
TCMID is composed of six data fields, namely prescriptions, herbs, ingredients, targets, drugs and diseases. The information and data in those fields were integrated from related web-based databases and text mining of books and published articles.
The prescriptions were collected mainly through text mining from books and published articles. Information for herbs was mainly extracted from TCM-ID database and referred to a book—Encyclopedia of Traditional Chinese Medicines (15 ). The data field about herbal ingredients, such as name and structure, was inputted by combining information from TCM@Taiwan, TCM-ID and Encyclopedia of Traditional Chinese Medicines. Information of diseases and their related proteins, drugs and their targets was retrieved from DrugBank and OMIM. As the target ID used by DrugBank, OMIM and other sources are different from each other, the data from those resources are inconsistent and incomparable. To overcome the barriers, we converted all of them into UniProt AC—a comprehensive, high-quality and freely accessible resource of protein sequence and functional information (16 (link)).
The main goal of our system is to build the connections between the herbal ingredients and diseases through disease genes/proteins, which could also be potential drug targets. To this end, we applied three different methods as follows:
First, we used the information supplied by STITCH (17 (link)), an aggregated database of interactions connecting >300 000 chemicals and 2.6 million proteins. We used the herbal ingredients’ general names and other alternative names to search STITCH and retrieved the related targets (protein); we then converted the corresponding target’s id into UniProt AC for unification purpose.
Second, the information from Herb Ingredients' Targets (HIT), which is extracted from published articles, was collected and integrated into our database.
Finally, as the information from HIT is mainly extracted from articles published in English, while the major TCM researches are in China, and the related research results are mainly published in Chinese, we collected these related articles published in Chinese and manually extracted the related information of ingredients and their targets from them. We used those herb names we collected and one of the following keywords ‘target’, ‘mechanism’, ‘pharmacology’ and ‘pharmacological’ to search Weipu database, which is like PubMed and is a system to host abstracts for the published articles in Chinese. Totally, we manually collected 680 herbal targets from >4500 articles. We also recorded the descriptions for the related experimental evidences and related URL or title for each article.
The six data fields in our database system are connected with their intrinsic relations (Figure 1): a prescription is composed of herbs, a herb contains various ingredients (compounds), an ingredient (or a drug) can interact with its targets (proteins) and a disease could be caused by the dysfunction of genes/proteins.

Database structure. A–E: six data fields for prescription, herbs, ingredients, diseases, targets and drugs, respectively. 1–5: relationship used to connect each other. 1: prescription is composed of herbs. 2: herb contains ingredients. 3: ingredients can interact with targets. 4: drugs have identified targets. 5: targets may be the causes of disease.

Xue R., Fang Z., Zhang M., Yi Z., Wen C, & Shi T. (2012). TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Research, 41(Database issue), D1089-D1095.
Publication 2012
Amino Acid Sequence Chinese Drug Delivery Systems Gene Products, Protein Pharmaceutical Preparations Proteins
4
Nationwide Cohort Study Using Taiwan NHIRD

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2017
Childbirth Compulsive Behavior Diagnosis Health Insurance Hospitalization National Health Insurance National Health Programs Outpatients Patient Discharge Patient Representatives Patients
5
Identifying Depression in Healthcare Data
The following distinct sources of the SNIIRAM were used to select persons with depression:

Diagnoses of long-term or costly conditions (Affections de Longue Durée, ALD). Patients with specific long-term or costly conditions may require full coverage for all their condition-related health expenditures upon request by their family doctor and after approval by a health insurance fund medical officer (médecin-conseil) [18 ].

Data from national hospital claims (Programme de Médicalisation des Systèmes d’Information, PMSI) for all inpatient and day-case admissions in public and private general and psychiatric hospitals, containing medical diagnoses defined as ICD-10 codes. In both general and psychiatric hospitals, a principal diagnosis is defined as the main reason for admission, while associated diagnoses provide information about conditions that significantly influenced care during the hospital stay [19 ].

Data concerning all national health insurance reimbursements for drugs, laboratory tests and outpatient medical procedures. Individuals receiving reimbursements for antidepressants (N06A section of the ATC classification except for oxitriptan) can be identified. However, these databases do not contain direct information about the diagnosis justifying the prescription, and these drugs are not specific for depression, as they can also be prescribed for other conditions (bipolar disorders, anxiety or chronic pain). An antidepressant prescription is typically valid 1 month.

All three sources were not considered to be equally reliable for identifying patients with depression. Reliability of the sources was assessed as follows: for the purpose of identifying individuals suffering from depression, full coverage for depression as a specific long-term or costly condition (source 1) was more reliable than the hospital claims database (source 2), which was more reliable than reimbursement for antidepressants (source 3). In the hospital claims database, associated diagnoses reported during general hospital stays were assumed to be less reliable than those reported during psychiatric hospital stays. The reasons underlying this classification of source reliability included (1) the mode of acquisition of the information (diagnoses resulting from medical interviews were regarded as more reliable than hospital diagnostic codes sometimes coded by non-medical staff, themselves regarded as a more reliable diagnostic markers than prescription drugs) and (2) what was as stake when the information was coded (hospital diagnostic codes that had no consequence on costs were regarded as less reliable than codes influencing costs or giving access to benefits). These reasons are described and discussed more thoroughly in the Merits and drawbacks of the various methods section of the Discussion section of this article.
Accordingly, five estimation methods with decreasing order of reliability were defined. ICD-10 codes F32 to F39 were used in all estimation methods to identify depression (either as a full health coverage code or as a principal or associated diagnosis). At least three reimbursements for antidepressants were used to identify treatment by antidepressant. Hospital stays in the last 5 years with a principal or associated diagnosis of depression were used to identify principal diagnosis history and associated diagnosis history of depression respectively.

Method A (Full coverage for depression): Selection of individuals with full coverage for depression as a specific long-term or costly condition during the study (source 1);

Method B (Hospitalisation for depression): Selection of individuals with depression as principal or associated diagnosis in a psychiatric hospital stay or as principal diagnosis in a general hospital stay using two timeframes: (a) the current calendar year and (b) the last two calendar years (source 2). Calendar years were used for technical reasons.

Method C (Current antidepressant treatment + History of hospitalisation during the past 5 years): Selection of individuals treated by antidepressant and with a general hospital principal diagnosis history of depression or a psychiatric hospital principal or associated diagnosis history of depression (combination of sources 2 and 3);

Method D (Hospitalisation in a general hospital with an associated diagnosis of depression): Selection of individuals with depression as associated diagnosis in a general hospital stay using two timeframes: (a) the current calendar year and (b) the last two calendar years (source 2);

Method E (Current antidepressant treatment + History of hospitalisation in a general hospital with an associated diagnosis of depression during the past 5 years): Selection of individuals treated by antidepressant and with a general hospital associated diagnosis history of depression (combination of sources 2 and 3).

Individuals with a hospital diagnosis of bipolar disorder (ICD-10 codes F30 or F31) in the last 5 years or a specific treatment for bipolar disorder (lithium, divalproex or valpromide) were not included in the study.
Filipovic-Pierucci A., Samson S., Fagot J.P, & Fagot-Campagna A. (2017). Estimating the prevalence of depression associated with healthcare use in France using administrative databases. BMC Psychiatry, 17, 1.
Full text: Click here
Publication 2017
5-Hydroxytryptophan Antidepressive Agents Anxiety Bipolar Disorder Chronic Pain Diagnosis Diagnosis, Psychiatric dipropylacetamide Health Insurance Hospitalization Inpatient Insurance, Health, Reimbursement Lithium Medical Staff Outpatients Patients Pharmaceutical Preparations Physicians Prescription Drugs Valproic Acid

Most recents protocols related to «Prescriptions»

1
Chromatic Aberration Regulation for Myopia Control
Not available on PMC !

Example 24

In this example, an alternate method of correction of the exemplary −2D myopic model eye is provided using two pairs of spectacle lenses (FIG. 36 and FIG. 37). By alternating the pairs of these spectacle lenses over a defined time period, the prescription introduces a temporal variation in the longitudinal and/or transverse chromatic aberration experienced at the M and/or L cone receptors, which contribute towards contradictory optical signals at the retinal level that may inhibit/control the progression of myopia. In other exemplary embodiments, the defined time period may be 1 hour, 6 hours, 12 hours, 24 hours or 48 hours.

Other exemplary embodiments are set forth in the following examples.

US11874532B2. Devices, systems and/or methods for myopia control (2024-01-16). Brien Holden Vision Institute Limited [AU]. Inventors: Ravi Chandra Bakaraju [AU], Klaus Ehrmann [AU], Cathleen Fedtke [AU], Padmaja Sankaridurg [AU], Arthur Ho [AU].
Full text: Click here
Patent 2024
Cardiac Arrest Eyeglasses Lens, Crystalline Myopia Myopia, Progressive Retina Retinal Cone Vision
2
Ophthalmic Lens with Multifocal Optical Power
Not available on PMC !

Example 4

FIG. 8 illustrates an exemplary embodiment of the power profile of an optic zone for a lens. The example of FIG. 8 is directed to an ophthalmic lens comprising:

an optic zone comprising:

a primary area 301 having a primary optical power;

a central portion 311;

a first secondary area 302 within the central portion 311 having a first secondary optical power;

a first power transition area 304 having a first power transition from the primary area 301 to the first secondary area 302;

a peripheral portion 310;

a second secondary area 303 within the peripheral portion 310 having a second secondary optical power; and

a second power transition area 305 having a second power transition from the primary area 301 to the second secondary area 303;

wherein the primary optical power is selected according to a prescription for refractive correction, the first secondary optical power is more positive than the primary optical power and the second secondary optical power is more positive than the primary optical power;
wherein the first power transition comprises: at least a first step 306 in the first power transition area 304 in which the rate of change in power, from the first secondary optical power in the first secondary area 302 to the primary optical power in the primary area 301, changes at a first junction 313 between a first transition region 312 within the first power transition 304 and the first step 306 followed by a change in the rate of change in power at a second junction 314 between a second transition region 315 within the first power transition 304 and the first step 306, and
at least a second step 307 and a third step 308,
wherein the second step 307 lies within the second power transition area 305 in which the rate of change in power, from the second secondary optical power in the second secondary area 303 to the primary optical power in the primary area 301, changes at a third junction 318 between a third transition region 319 within the second power transition 305 and the second step 307 followed by a change in the rate of change in power at a fourth junction 317 between a fourth transition region 316 within the second power transition 305 and the second step 307, and the third step 308 lies within the second power transition area 305 in which the rate of change in power, from the second secondary optical power in the second secondary area 303 to the primary optical power in the primary area 301, changes at a fifth junction 321 between a fifth transition region 321 within the second power transition 305 and the third step 308 followed by a change in the rate of change in power at a sixth junction 320 between the third transition region 319 within the second power transition 305 and the third step 308.

In the exemplary embodiment of FIG. 8, the power of the primary area 301 is approximately −2 D and has a progression in optical power progressively increasing in positive power towards the periphery. Such peripheral progressive increase in power may result in effective or improved visual performance or vision performance in one or more aspects of visual performance or vision performance. For example, spherical aberration may be included in the primary area to correct, reduce or manipulate aberration of the eye and ophthalmic lens combined. Such an exemplary inclusion of spherical aberration may improve clarity of vision, contrast, contrast sensitivity, visual acuity, and overall quality of vision or combinations thereof.

In certain embodiments, the power of a primary area may be constant, substantially constant, progressively increasing, progressively decreasing, modulated (i.e. undulating along its power profile), possess an aberration profile (e.g. spherical aberration) or combinations thereof.

In the exemplary embodiment of FIG. 8, the powers of the first step 306, second step 307 and third step 308 are not constant within the steps.

In certain embodiments, the power profile within a step may be constant, or substantially constant, or progressively changing. In certain embodiments in which the power of a step is progressively changing, the change in power across the width of the step may be between 0 and 0.2 D, 0 and 0.15 D or 0 and 0.1 D. In certain embodiments in which two or more steps have progressively changing power profiles, the rate of change of the power profiles between the two or more steps may be equal or unequal.

In the exemplary embodiment of FIG. 8, the power profile along the first power transition 304 and the second power transition 305 are monotonic.

Monotonic means that where a power transition decreases from one area to another area (for example, between a first secondary area and a primary area), the power profile is either decreasing or constant or substantially decreasing or substantially constant along the power transition including steps within the power transition. Conversely, where a power transition increases from one area to another area (for example, from a primary area to a second secondary area), monotonic means the power profile is either increasing or constant or substantially increasing or substantially constant along the power transition including steps within the power transition. In certain embodiments, a power transition will have a monotonic power profile.

In the exemplary embodiment of FIG. 8, changes in the rate of change in optical power at junctions 313 and 314 that forms the first step 306 and changes in the rate of change in optical power at junctions 317 and 318 that forms the second step 307 are less rapid and/or more gradual.

In certain embodiments, a change in the rate of change in optical powers may be considered “gradual” when the change in rate of change occurs over a junction width of between 0.15 and 1 mm, 0.25 and 0.75 mm or 0.3 and 0.5 mm.

US11874529B2. Ophthalmic optical lens for vision correction having one or more areas of more positive power (2024-01-16). Brien Holden Vision Institute Limited [AU]. Inventors: Brien Anthony Holden [AU], Padmaja Rajagopal Sankaridurg [AU], Klaus Ehrmann [AU], Fabian Conrad [AU], Arthur Ho [AU].
Full text: Click here
Patent 2024
A-301 Contrast Sensitivity Disease Progression Lens, Crystalline Ocular Refraction Visual Acuity
3
Comprehensive Psychiatric Data Collection
Detailed medical information about previous illness, medication usage, hereditary dispositions, drug, tobacco, and alcohol intake will be acquired for all participants through interviews, self-report questionnaires, electronic medical records (EMR), and registry data.
Data extracted from the EMR will include treatment codes from the MDD treatment package, dates for treatment package start and completion, psychiatric comorbidities; and standard clinical blood work (e.g., HBA1c, TSH, CRP, and cholesterol). In addition, hormonal contraceptive and psychotropic medication prescription and usage (from 1995 onward) will be extracted from the Danish National Prescription Registry [55 (link), 56 ]. This information includes prescribed medication and dosage and when the patient redeems a prescription. We will retrieve information on lifetime comorbidity from The Danish National Patient Registry (DNPR) [57 (link)]. From the Medical Birth Registry, we will obtain data on maternal and maternal perinatal health [58 (link)]. We will also collect information on alcohol and drug abuse treatment from the National Registry of Alcohol Treatment and Registry of Drug Abusers Undergoing Treatment. From the social registers in Statistics Denmark, we add data on marital status, occupational history, ethnicity, and educational level [59 (link)].
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Full text: Click here
Publication 2023
Abuse, Alcohol BLOOD Cholesterol Contraceptive Agents Drug Abuser Ethanol Ethnicity Mothers Patients Pharmaceutical Preparations Psychotropic Drugs Tobacco Products
4
Long-Term Longitudinal Psychiatric Outcomes
After the last 18-month follow-up, the study dataset will be sent to Statistics Denmark with a list of all invited participants to allow a non-participant analysis and long-term follow-up. The study data will be linked with data from the Danish Civil Registration System [76 (link)] e.g., the DNPR [57 (link)], the Danish National Prescription Registry [55 (link), 56 ], and other registries indexing, e.g., hospital admittance, diagnosis- and treatment codes, prescription medications, employment status, living situation (e.g., partner information), and income. We will also examine diagnostic stability [77 (link)], e.g., change of primary diagnosis from first episode depression to an anxiety or personality disorder or later recurrent depressive episode or conversion to bipolar affective disorder.
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Full text: Click here
Publication 2023
Anxiety Disorders Diagnosis Disorder, Dissociative Personality Disorders Prescription Drugs
5
Comprehensive Medication Review and Management
Before randomization, comprehensive medication reviews were conducted by BCGPs for all participants using participant-reported medical conditions and information on dose, frequency, indication, duration of treatment, tolerability, and adverse drug reactions for all prescription medications, vitamins, and supplements. The BCGP medication review process involved 1) assessing the clinical appropriateness of each medication using the Beers Criteria [13 ] and Medication Appropriateness Index (MAI); [16 (link)] 2) evaluating potential drug-drug and drug-disease interactions in accord with the above and also taking into account prescription label information; and 3) assessing whether medication regimens followed relevant disease-specific evidence-based guidelines [13 , 17 (link), 18 (link)]. Of note, blood laboratory work results, electronic medical records, and previous therapies (e.g., medication failures) were not available to BCGPs when devising baseline recommendations, but were available to the clinician member of the MTM team. Following randomization, the MTM recommendations were only shared with those participants randomized to the intervention group (N = 46). Recommendations for the control group were recorded in the study database but not shared with those participants.
During the INCREASE study period, the pharmacy team of two BCGPs utilized drug and health information resources (e.g., Lexicomp and UpToDate [Wolters Kluwer Health Inc. Riverwoods, IL]), Beers Criteria [13 ], relevant guidelines (e.g., Diabetes Standards of Care [17 (link)] and Clinical Practice Guidelines for Hypertension [18 (link)]), and clinical judgement to justify their recommendations. Each recommendation was reviewed by both BCGPs and a consensus pharmacy recommendation was decided via discussion. Detailed information for each recommendation was then entered into a series of pre-specified study protocol data collection forms, allowing for systematic categorization of recommendations as either: 1) medication discontinuation with or without tapering; 2) switch to a different medication; 3) dose adjustment (e.g., decrease dose, adjust dose for organ function/tolerability, or increase dose); 4) new medication initiation; 5) drug or disease monitoring recommendation (e.g., vital signs, falls risk, sedation); or 6) a non-pharmacologic recommendation (e.g., sleep hygiene, avoiding gastroesophageal reflux triggers, referral for diagnostic workup). Baseline recommendations were also categorized by pharmacologic class and over the counter (OTC) or supplement status of the medication prompting a baseline MTM recommendation. A full schematic for medication categorization is available in the supplementary material (see Supplementary Table S1).
Smith N.I., Martinez A.I., Huffmyer M., Eckmann L., George R., Abner E.L., Jicha G.A, & Moga D.C. (2023). Acceptability of patient-centered, multi-disciplinary medication therapy management recommendations: results from the INCREASE randomized study. BMC Geriatrics, 23, 137.
Full text: Click here
Publication 2023
BLOOD Clinical Reasoning Diabetes Mellitus Diagnosis Dietary Supplements Drug Interactions Drug Reaction, Adverse Drugs, Non-Prescription Gastroesophageal Reflux Disease High Blood Pressures Medication Review Pharmaceutical Preparations Precipitating Factors Sedatives Signs, Vital Treatment Protocols Vitamins

Top products related to «Prescriptions»

1
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
2
Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
3
Sas v9 by SAS Institute
Sourced in United States, Austria, United Kingdom, Cameroon, Belgium, Israel, Japan, Australia, France, Germany
SAS v9.4 is a software product developed by SAS Institute. It is a comprehensive data analysis and statistical software suite. The core function of SAS v9.4 is to provide users with tools for data management, analysis, and reporting.
4
Stata version 14 by StataCorp
Sourced in United States, Denmark, Austria, United Kingdom, Japan, Canada
Stata version 14 is a software package for data analysis, statistical modeling, and graphics. It provides a comprehensive set of tools for data management, analysis, and reporting. Stata version 14 includes a wide range of statistical techniques, including linear regression, logistic regression, time series analysis, and more. The software is designed to be user-friendly and offers a variety of data visualization options.
5
Eclipse by Agilent Technologies
Sourced in United States, Canada
The Eclipse is a versatile laboratory instrument designed for precise spectroscopic measurements. It features advanced optics and a robust design to provide reliable and accurate data.
6
Sas software by SAS Institute
Sourced in United States, Japan, Austria, Germany, United Kingdom, France, Cameroon, Denmark, Israel, Sweden, Belgium, Italy, China, New Zealand, India, Brazil, Canada
SAS software is a comprehensive analytical platform designed for data management, statistical analysis, and business intelligence. It provides a suite of tools and applications for collecting, processing, analyzing, and visualizing data from various sources. SAS software is widely used across industries for its robust data handling capabilities, advanced statistical modeling, and reporting functionalities.
7
R version 3 by R Project for Statistical Computing
Sourced in United States, Austria, Japan, Belgium, New Zealand, United Kingdom, Germany, Denmark, Australia, France
R version 3.6.1 is a statistical computing and graphics software package. It is an open-source implementation of the S programming language and environment. R version 3.6.1 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.
8
Sas software version 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, Brazil, United Kingdom, Cameroon
SAS software version 9.4 is a comprehensive data analysis and management solution. It provides advanced statistical and analytical capabilities for organizations to manage, analyze, and report on their data. The software includes a range of tools and features to support various data-driven tasks, such as data manipulation, statistical modeling, and predictive analytics.
9
Stata 14 by StataCorp
Sourced in United States, Denmark, United Kingdom, Belgium, Japan, Austria, China
Stata 14 is a comprehensive statistical software package that provides a wide range of data analysis and management tools. It is designed to help users organize, analyze, and visualize data effectively. Stata 14 offers a user-friendly interface, advanced statistical methods, and powerful programming capabilities.
10
Eclipse treatment planning system by Agilent Technologies
Sourced in United States, Germany, Canada
The Eclipse treatment planning system is a software tool designed for radiation therapy planning. It provides medical professionals with the capabilities to create and optimize treatment plans for cancer patients undergoing radiation therapy. The core function of the Eclipse system is to facilitate the planning and simulation of radiation treatment procedures.

More about "Prescriptions"

Prescriptions are essential documents in the healthcare industry, providing licensed healthcare providers a way to order medication or treatment for their patients.
These written orders play a vital role in ensuring patients receive the appropriate drugs or therapies to manage their health conditions.
Prescriptions typically outline the drug name, dosage, frequency, and administration method, helping to promote safe and effective medication use.
Understanding the significance of prescriptions is crucial for healthcare providers, pharmacists, and patients to optimize treatment outcomes and enhance patient safety.
Key aspects of prescriptions include the use of drug names, dosages, administration routes, and frequency of use.
Abbreviations like Rx (prescription), Rx (take), and Sig (instructions) are commonly used.
Prescription optimization is an important consideration, with tools like SAS version 9.4, SAS 9.4, SAS v9.4, Stata version 14, Eclipse, SAS software, R version 3.6.1, SAS software version 9.4, Stata 14, and Eclipse treatment planning system offering support.
Whether you're a healthcare professional, researcher, or patient, understanding the nuances of prescriptions is essential for delivering the best possible care and outcomes.
Remember, a single typo can make a big difference, so attention to detail is key.