2014 data analysis report patient assistance

Is data analytic capacity the key to Medicaid system reform?

For many states, a critical barrier to achieving Medicaid delivery system reform is data analytic capacity.

Do NDAs and Blas contain the patient experience data table?

reviews of NME NDAs and BLAs in the cohort, 82% of those in the first year contain the Patient Experience Data Table, 77% of those in the second year contain the table, and 87% of those in the third contain the table. A few reviews contain a brief statement that no patient experience data were submitted instead of a table. June 18, 2021

What types of patient experience data does FDA mention in reviews?

Here ERG describes the types of patient experience data that FDA mentions in reviews of NME NDAs and BLAs: mainly Patient-Reported Outcomes (PROs) or other types of Clinical Outcome Assessments (COAs) (Table 2-3). PROs and other COAs are the types of patient experience data most likely to serve as endpoints in clinical

Is there a patient experience data table available?

included a Patient Experience Data Table as part of publicly available review documents. As required by the Cures Act, in 2021, 2026, and 2031, FDA will conduct assessments of its use of patient experience data in regulatory decision-making, in particular with respect to the review of patient

What is AAPOR cooperation rate?

The AAPOR Cooperation Rate is the number of complete and partial complete interviews divided by the number of contacted and eligible respondents. The BRFSS Cooperation Rate follows the guidelines of AAPOR Cooperation Rate #2. Separate cooperation rates are calculated for landline telephone and cellular telephone samples for each state and territory.

What is response rate calculation?

On the basis of the AAPOR guidelines, response rate calculations include assumptions of eligibility among potential respondents or households that are not interviewed. Changes in the geographic distribution of cellular telephone numbers by telephone companies and the portability of landline telephone numbers are likely to make it more difficult than in the past to ascertain which telephone numbers are out-of-sample and which telephone numbers represent likely households. The BRFSS calculates likely households using the proportions of eligible households among all phone numbers where eligibility has been determined. This eligibility factor appears in calculations of response-, cooperation-, resolution-, and refusal rates.

What is the refusal rate in BRFSS?

The BRFSS Refusal Rate is the proportion of all eligible respondents who refused to complete an interview or terminated an interview prior to the threshold required to be considered a partial interview. Refusals and terminations (TERE) are in the numerator, and the denominator includes all eligible numbers and a proportion of the numbers with unknown eligibility. The proportion of numbers with unknown eligibility is determined by the eligibility factor (E as described above). The result is then multiplied by 100 to provide a percentage of refusals among all eligible and likely to be eligible numbers in the sample. Separate refusal rates are calculated for landline telephone and cellular telephone samples for each state and territory.

What states does IAP provide data analytics?

IAP provided data analytics technical assistance to Medicaid agencies in Alabama, Guam, Mississippi, Nebraska, New Jersey, North Dakota, Commonwealth of the Northern Mariana Islands (CNMI), Pennsylvania, Washington, and West Virginia from April 2017 through April 2018. Additional information about the technical assistance provided can be found in ...

What is IAP technical assistance?

In addition to the one-on-one technical assistance, IAP developed use cases, tools, and webinars to share solutions and information on data analytic-related issues that could be of interest to all states.

What is the Medicaid Innovation Accelerator Program?

Within the functional area of data analytics, the Medicaid Innovation Accelerator Program (IAP) offered targeted technical assistance and solutions to Medicaid agencies in building and strengthening their data analytic capacity as they design and implement delivery system reforms. Below is information about the technical assistance, as well as many data analytics resources for states such as Medicare-Medicaid data integration use cases, tools for building data dashboards with effective visualizations, resources to improve data quality, and examples of analysis addressing specific topics.

When will IAP be available for Medicaid?

IAP provided technical assistance to Medicaid agencies from Delaware, Kentucky, Massachusetts, North Carolina, South Dakota, Texas, and Wyoming from March 2020 through September 2020. Additional information about the technical assistance provided can be found in the Medicaid IAP MM/SMM Data Analytics Factsheet (PDF, 188.91 KB).

What states does IAP work with?

IAP provided data analytics technical assistance to Medicaid agencies in Connecticut, the District of Columbia, Florida, Georgia, Guam and CNMI, Virginia, and West Virginia from March 2020 through September 2020.

What is the National Practitioner Data Bank?

11101 et seq.), led to the establishment of the National Practitioner Data Bank (NPDB). Title IV authorized the NPDB to collect and disclose to authorized queriers certain information relating to the professional competence and conduct of physicians, dentists, and other health care practitioners. Subsequent laws later expanded the information collected and disclosed by the NPDB and modified its operations. Most recently, Congress passed Section 6403 of the Patient Protection and Affordable Care Act of 2010, Public Law 111-148 to eliminate duplication between the NPDB and the Healthcare Integrity and Protection Data Bank (HIPDB). On May 6, 2013, NPDB operations were consolidated with those of the former HIPDB. As a result of this consolidation, information previously collected and disclosed by the HIPDB is now collected and disclosed by the NPDB. This legislation established the NPDB as the single Data Bank to receive and disclose information collected under Title IV, Section 1921 of the Social Security Act, and Section 1128E of the Social Security Act. Information is available to eligible entities based on the requirements of each law. As of May 6, 2013, this Data Analysis Tool contains practitioner reports received by the NPDB and includes state licensure and certification actions, clinical privileges/panel membership and professional society membership actions, and HHS/OIG and DEA actions.

What is an AAR report?

Adverse Action Report (AAR) - the report format used to submit actions, other than medical malpractice payments and convictions and judgments, taken against a health care practitioner, entity, provider, or supplier. AARs in this Data Analysis Tool reflect actions against health care practitioners only.

What is NPDB data?

The National Practitioner Data Bank (NPDB) routinely collects information relating to medical malpractice payments and certain adverse actions taken by hospitals and other health care entities, professional societies, health plans, peer review organizations, private accreditation organizations, Federal and State licensing and certification authorities, and certain other Federal and State agencies. More information about the NPDB can be found in About Us. The report level data used in this data analysis application reflect medical malpractice payment and adverse action information, including state licensure and certification actions, clinical privileges/panel membership and professional society membership actions, and HHS/OIG and DEA actions. The term "unique practitioner" is used under the NPDB Practitioners tab to denote the following: one practitioner can be counted in multiple types and in multiple states, which would cause a summation of either category to be greater than the actual unique practitioner count.

What is HCQIA 426?

Under section 426 of the HCQIA (42 USC 11135), as implemented by regulations at 45 CFR part 60.13 (a) (2) (ix), data may be released to "a person or entity who requests information in a form which does not permit the identification of any particular health care entity, physician, dentist, or other health care practitioner." This information is released in accordance with that provision to facilitate research use of NPDB information by persons interested in medical malpractice, licensing, discipline, and quality assurance issues.

How to access NPDB data?

An accessible version of NPDB data is available by downloading the Public Use Data File .

What is a DAT?

The Data Analysis Tool (DAT) allows you to generate datasets for Adverse Action Report (AAR) and Medical Malpractice Payment Report (MMPR) data for 1990 through June 30, 2021. You may tailor your data by using the filters available or by clicking on the map or graph. Hover over a state on the map to see detailed information for that state.

Can a dataset be used for statistical analysis?

The dataset can only be used in connection with statistical reporting or analysis.

How does big data impact healthcare?

Recently, big data is shifting the traditional way of data delivery into valuable insights using big data analytics method. Big data analytics provides a lot of benefits in the healthcare sector to detect critical diseases at the initial stage and deliver better healthcare services to the right patient at the right time so that it improves the quality of life care. Big data analytics tools play an essential role to analyze and integrate large volumes of structured, semistructured and unstructured vital data rapidly produced by the various clinical, hospitals, other social web sources and medical data lakes. However, there are several issues to be addressed in the current health data analytics platforms that offer technical mechanisms for data collection, aggregation, process, analysis, visualization, and interpretation. Due to lack of detailed study in the previous literature, this article inspects the promising field of big data analytics in healthcare. This article examines the unique characteristics of big data, big data analytical tools, different phases followed by the healthcare economy from data collection to the data delivery stage. Further, this article briefly summarizes the open research challenges with feasible findings, and then finally offers the conclusion.

What is healthcare analytics?

Healthcare Analytics collects the data from a myriad of areas such as clinicians, hospitals, government agencies, health insurance, pharmaceutical, and biotechnology agencies and allows for the examination of trends and patterns in various healthcare data.

Why is the amount of data increasing?

These days, amount of data in different formats is increasing rapidly due to the use of different technologies and increasing use of Internet . In previous decades, data even if it is large, its format and sources are limited but now-a-days, massive amount of data is collected from different sources in different formats. This concept gave rise to new concept called “Big Data” which is a present trend to deal with the data. Analytics is one more crucial topic under big data which deals with the analysis and its integration with business process. Many books, tools, sub topics were raised from the “Big Data” where it takes a large amount of time to understand and to start to work with it. Hence, we are going to give a review on “Big Data”, “Big Data Analytics” and its tools briefly. Here, Healthcare is taken as example to get the brief understanding on “Big Data and Analytics”. This paper, we have also reviewed various big data frameworks with respective to data sources, application area, analytical capability and made study on various papers by presenting their methodology, tools, advantages and limitations.

Why is large scale data important?

Large scale data have been proven of great importance in healthcare, and therefore there is a need for advanced forms of data analytics , such as diagnostic data and descriptive analysis, for improving healthcare outcomes.

How has in-memory data management changed the world?

In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, and cloud computing are serving as the foundation for the transition of enterprises away from this restrictive model. In this book, we describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT professionals and IT managers, but it is also for senior management who wish to create new business processes by leveraging in-memory computing.

Why are technology costs so high in small clinics?

technology in small clinics and organizations, with high costs due to reduced efficiencies of scale.

How does information technology help the health care system?

For the American health care system to benefit from advances in IT, it must adopt electronic health records (EHRs). An EHR contains the complete medical history of a patient, including a full listing of illnesses, laboratory tests, treatments, drugs administered, and allergies. Health IT is not just about merely digitizing medical records to create a paperless office, although doing this will achieve considerable savings - it is also about fundamentally transforming the health care system so that both doctors and patients have access to information and tools that allow them to better manage their care. This new IT-enabled model of health care has the potential to improve preventive health care and chronic disease management and reward medical practices with financial incentives for effective and efficient care. It has the potential to give health care researchers the data they need to identify and deliver best practice care and continuously improve the quality of health care. Finally, health IT has the potential to empower consumers to better understand and manage their own health care conditions, needs, and treatments. This paper explores the benefits of using information technology in the health care sector, such as reduced medical costs, improved medical care, and increased access to personal health information. It then reviews the obstacles that have prevented the widespread adoption of EHRs and proposes a number of policy recommendations to speed adoption. Specifically, the paper discusses the benefits of establishing independent health record data banks as a sustainable and market-based approach to implementing EHRs. ITIF also recommends other methods to leverage federal resources to speed EHR adoption.