Blog Archives - TranscribeMe

Why Annotated Data is So Important to Machine Learning

Transcribe Me — Wed, 26 Jul 2023 20:33:13 +0000

TranscribeMe creates structured data sets for customers to use to create or enhance machine learning models.

Before getting to case studies illustrating this work, some terms need to be either defined or clarified, i.e., “structured data” and “AI.”

I consider AI to be a misnomer. Intelligence is intelligence; excluding all other flora and fauna, it divides into human or machine. So for me, there’s nothing artificial about an intelligent machine. It’s simply not human.

Learning Through Structured Data

Consider how humans learn. A newborn is pretty much helpless, but from birth it packs an enormously powerful and complex brain that from day one is collecting, integrating, and assimilating environmental data, including speech. Without speech, the child is in stealth mode, but the right brain is hyper engaged in an activity that data scientists would call unsupervised learning.

As the child grows, structured data is introduced in the form of books. Initially, a parent may read to the child and point out elements in the story. For example, while reading “Goodnight Moon,” the parent might say, “Moon,” then point to its picture, tying the word to a visual. That is data annotation!

As children continue to learn, the enormous capacity of the brain to log, store, and collate data comes into play and the children become, for the most part, autonomous learners.

A newborn machine has neither a right brain, nor the nearly unlimited data capacity of a human brain to begin learning and storing data. It’s estimated that a human brain can store 2.5 petabytes of information. That would be equivalent to a DVR recording continuously for 300 years!

A newborn machine begins its quest for intelligence at the Goodnight Moon stage where a pairing takes place: an audio recording of the word “moon” with the written word, or an image of the moon with an audio recording of the word.

As is the case with the child learner, this is data annotation.

An example of structured data could be, let’s say, a complex set of data defining all North American songbirds at the exclusion of all else. This would produce an intelligent machine that could identify every single songbird on the continent. But it couldn’t tell us a thing about butterflies! And there would be nothing in its database or algorithmic logic to take it from songbird to butterfly.

A new set of structured data must be created and assimilated for every new thing we want our machine to learn. It’s always been this way from the beginning of time, machine learning time, that is.

Here’s a quote from Wikipedia in the article, Expert System: “In the late 1950s… biomedical researchers started creating computer-aided systems for diagnostic applications in medicine and biology. These early diagnostic systems used patients’ symptoms and laboratory test results as inputs to generate a diagnostic outcome.” Even for the first machines, data annotation was required.

From the 1950s until now, all machine learning has required data annotation to create structured datasets to create or enhance machine learning models. There have been many claims of unsupervised learning, but that has not been true in cases we’ve seen. The machines have gotten more sophisticated with their data collection, but overall the machine needs to be trained for a specific use.

Use Cases for Annotated Data

Every day AI and machine learning technologies are delivering astounding accomplishments that benefit a broad spectrum of fields and people around the world, including encompassing areas such as software and development, cybersecurity, medicine, engineering, customer service, finance, manufacturing, and more.

But scientists, technologists, and huge industries are not the only ones reaping the benefits of machine learning. Small businesses and individuals alike are beginning to understand that data collection and analysis are now the norm, so it is no wonder that AI and machine learning are among the fastest growing technologies globally.

These technologies include audio, images, videos, podcasts and more. Simply put, data is labeled to make it comprehensible to AIs. The key is the accuracy of the data sets and the quantity of data sets is also very important so that there is increased variety in the verbiage and context.

This is where TranscribeMe comes in. We have been asked to provide annotated data for a variety of use cases. And we have teams that are specially trained to label and process data appropriately for any given project. Here are just a few examples:

Medical Services

Topic: Medical Emergency Screening
Form of Data Annotation: Audio
Process: Annotators listen to agonal breathing recordings and mark the beginnings and ends of the wavelengths.
Purpose: To be able to teach the provider’s automated system to screen patient calls for agonal breathing in order to identify callers who are experiencing a heart attack or stroke.

Fast Food Industry

Topic: Accuracy of Automated Orders
Form of Data Annotation: Audio/text
Process: Customers’ drive-thru orders are transcribed.
Purpose: To train the restaurant’s automated system to recognize drive-thru orders that are placed by learning to recognize menu items regardless of customers’ accents and despite high levels of surrounding noise.

Telephony Company

Topic: Customer Service Analysis
Form of Data Annotation: Text
Process: Specific labels are used to tag words or phrases in pre-transcribed customer service conversations.
Purpose: To build custom speech models for call center use cases by identifying customer sentiment, logging why customers call, as well as how the calls end, and by qualifying the agents’ responses.

Court Stenography Company

Topic: Annotation via Keywords
Form of Data Annotation: keyword spotting
Process: Words and phrases from notices of depositions are tagged according to keywords per the clients’ instructions.
Purpose: To compile data sets from deposition notices using keywords that identify plaintiffs, defendants, witnesses, attorneys, deposition location, date, time, and other similar information.

Self-Driving Vehicle Manufacturer

Topic: Passenger Safety
Form of Data Annotation: image tagging
Process: Annotators use special software to draw a shape around specific images in photos and videos.
Purpose: Tagged images are used to teach self-driving vehicles to avoid obstacles in the road such as potholes, cracks, water, etc.

We Train ASR’s

As technology advances and as more general transcribed audio becomes available on the net, ASR systems can scrape this data and self-train to a degree. We’re currently working with a company that is actively doing this and has produced very good results–but not great results. Consequently, they have come to us to acquire what is considered the gold standard in training data–human transcribed and annotated audio to text. That human factor is what it takes to make a good ASR a much better ASR.

Ledley RS, and Lusted LB (1959). “Reasoning foundations of medical diagnosis”. Science. 130 (3366): 9–21. Bibcode:1959Sci…130….9L. doi:10.1126/science.130.3366.9. PMID 13668531

Weiss SM, Kulikowski CA, Amarel S, Safir A (1978). “A model-based method for computer-aided medical decision-making”. Artificial Intelligence. 11 (1–2): 145–172. doi:10.1016/0004-3702(78)90015-2

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What is AI Training Data & Why Is It Important?

Transcribe Me — Fri, 21 Jul 2023 19:55:09 +0000

Artificial intelligence (AI) is a rapidly evolving field that has the potential to transform numerous industries and improve our daily lives. However, building an effective AI system requires the use of high-quality training data. In this blog post, we will explore what AI training data is and why it is essential for AI development.

What is AI Training Data?

AI training data is a set of labeled examples that is used to train machine learning models. The data can take various forms, such as images, audio, text, or structured data, and each example is associated with an output label or annotation that describes what the data represents or how it should be classified.

Training data is used to teach machine learning algorithms to recognize patterns and make predictions. By feeding a large amount of data with known labels into a machine learning algorithm, the algorithm can learn to recognize patterns and make predictions about new, unseen data.

Why is AI Training Data Important?

The quality and quantity of training data sets are crucial to the accuracy and effectiveness of machine learning models. The more diverse and representative the data is, the better the model can generalize and perform on new, unseen data. Conversely, biased or incomplete training data can result in inaccurate or unfair predictions.

For example, imagine the AI system is trained to recognize human voices but only on data from a single gender or accent. Such a system is likely to perform poorly on folks from other regions or have different accents. This is why it is crucial to carefully select and preprocess training data, ensuring that it represents the target population and is labeled accurately and consistently.

Additionally, training data can help mitigate the risk of AI bias. Bias in AI can occur when the training data is not representative of the target population or when the labeling process is biased. This can lead to unfair or discriminatory predictions, such as denying loans or job opportunities based on factors like race or gender.

By ensuring that the training dataset is diverse and representative and by using unbiased labeling processes, we can reduce the risk of AI bias and ensure that AI systems are fair and accurate.

What Are the Three Types of AI Training Data?

The three types of AI training data are:

Supervised learning datasets

Supervised learning is the most common type of machine learning, and it requires labeled data. In supervised learning, the training data consists of input data, such as images or text, and associated output labels or annotations that describe what the data represents or how it should be classified.

Unsupervised learning datasets

Unsupervised learning is a type of machine learning where the data is not labeled. Instead, the algorithm is left to find patterns and relationships in the data on its own. Unsupervised learning algorithms are often used for clustering, anomaly detection, or dimensionality reduction.

Reinforcement learning datasets

Reinforcement learning is a type of machine learning where an agent learns to make decisions based on feedback from its environment. The training data consists of the agent's interactions with the environment, such as rewards or penalties for specific actions.

Benefits of High-Quality AI Training Datasets

There are quite a few benefits of high-quality AI training datasets:

Improved accuracy and reliability

High-quality training data can improve the accuracy of machine learning models. When a model is trained on diverse, representative, and accurate data, it can better recognize patterns and make more accurate predictions on new, unseen data.

Faster model training time & development

High-quality training data can accelerate the development of machine learning models. With access to high-quality data, developers can quickly iterate and improve their models, reducing the time and resources required for development.

Better generalization

High-quality training data can improve the generalization ability of machine learning models. When a model is trained on diverse data, it can better adapt to new, unseen situations and perform well in real-world scenarios.

Reduced bias

High-quality training data can help reduce bias in machine learning models. By ensuring that the training data is diverse and representative, and by using unbiased labeling processes, we can reduce the risk of AI bias and ensure that AI systems are fair and accurate.

Challenges in Obtaining High-Quality AI Training Data

While high-quality AI training data is essential for building accurate, effective, and fair machine learning models, obtaining it can be challenging. Here are some of the challenges in obtaining high-quality AI training data:

Quality control: Ensuring the quality of the training data can be challenging, particularly when it comes to manual labeling. Human error, inconsistency, and subjective judgments can all impact the quality of the data.
Lack of availability: One of the biggest challenges in obtaining high-quality AI training data is the lack of availability. Data may be difficult or expensive to obtain, particularly for niche or sensitive domains.
Cost: Another challenge in obtaining high-quality AI training data is the cost. High-quality data can be expensive to acquire, particularly if it needs to be collected or labeled manually.
Data labeling: Depending on the problem being solved, obtaining high-quality AI training data may require extensive labeling efforts, which can be time-consuming and expensive.
Data volume: Obtaining enough high-quality data can be a challenge, particularly when it comes to deep learning models that require large amounts of data to achieve high accuracy.

FAQs About AI Training Data

Why is training data important in AI?

Training data is a fundamental component in the field of artificial intelligence (AI) as it serves multiple crucial purposes. First and foremost, training data allows AI models to learn patterns and relationships present in the data. By providing examples of input-output pairs, the model can identify underlying structures and correlations, enabling it to make accurate predictions or decisions when faced with new data.

Additionally, training data facilitates generalization – the model learns from a diverse range of examples to apply its understanding to previously unseen data. This ability to generalize is essential for AI systems to be useful in real-world scenarios.

What is training data vs test data AI?

Training data and test data are distinct subsets used for different purposes. Training data refers to the labeled dataset that is utilized during the training phase of an AI model. It consists of input examples paired with their corresponding desired outputs or labels. Essentially, the model learns from this training data by identifying patterns and relationships between inputs and outputs.

On the other hand, test data is a separate set of labeled examples that is withheld from the model during the training phase. This data is used to assess the performance and generalization capabilities of the trained model, and serves as an unbiased evaluation of the model’s ability to make accurate predictions or decisions on unseen data. It allows practitioners to estimate how well the model is likely to perform in real-world scenarios.

How do you get data for AI training?

There are several ways to obtain data for AI training. Here are some common approaches:

Public datasets: There are numerous publicly available datasets that you can utilize for AI training. These datasets cover a wide range of domains and tasks, including computer vision, natural language processing, speech recognition, and more. Examples of popular public datasets include ImageNet, COCO, MNIST, CIFAR-10, and IMDb.
Data collection: Depending on the specific problem you are addressing, you might need to collect your own data. This can involve designing surveys, conducting experiments, or creating data collection pipelines. For instance, if you are building a sentiment analysis model for customer reviews, you might gather relevant data by scraping websites or obtaining permission to access certain databases.
Data partnerships: Collaborating with organizations or individuals who have access to the data you need can be a viable option. Establishing partnerships allows you to leverage existing data sources that align with your AI project. This approach is particularly useful when dealing with proprietary or domain-specific data.
Data labeling: In many AI applications, labeled data is essential for supervised learning. Data labeling involves assigning the correct labels or annotations to the input data. You can perform the labeling process manually or use crowdsourcing platforms, where workers label the data based on predefined guidelines. It is important to ensure the quality and accuracy of labeled data.

What is the purpose of training data?

The ultimate objective of training is to enable the model to generalize its learning to new, unseen data. Training data helps the model acquire the ability to make accurate predictions or decisions on inputs that were not part of the training dataset. The model learns from the training data’s diverse examples to understand the commonalities and characteristics that are applicable beyond the specific training set.

Additionally, this type of data provides examples that allow the AI model to identify patterns, correlations, and relationships between input features and corresponding outputs. By analyzing the training data, the model learns to recognize the underlying structures and features that are relevant to the task it is being trained for.

Why is training important in machine learning?

Training is crucial in machine learning because it is the process through which models learn from labeled data and acquire the ability to make accurate predictions or decisions. It also allows models to optimize their performance by adjusting their internal parameters. By comparing their predictions to the known correct outputs in the training data, models iteratively refine their parameters to minimize errors and improve accuracy.

Training also empowers machine learning models with adaptability and scalability – models learn to adapt to changing environments and new data by updating their knowledge and adjusting their predictions based on new information. This adaptability ensures that models remain relevant and effective in dynamic scenarios, accommodating evolving data patterns.

How much training data does AI need?

The amount of training data required for AI can vary depending on several factors, including the complexity of the task, the complexity of the AI model, and the variability present in the data.

In general, more training data tends to improve model performance and generalization. However, there is a diminishing return on performance improvement as the dataset size increases. The amount of training data required can vary widely depending on the specific task and model. It is advisable to start with a sufficient amount of data and iteratively evaluate the model’s performance to determine if additional data is needed.

Our AI Training Datasets & Machine Learning Services

Successful artificial intelligence and machine learning models require transcriptions that are specifically formatted for your use case and AI system. We have robust, specially trained teams for these types of AI transcriptions, making it possible to build and scale quickly to meet your needs and transcribe your audio into a structured format specific to your machine learning requirements.

Contact us for a quote today.

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The U.S. Court Reporter Shortage Creates A Need For Transcription Services

Transcribe Me — Wed, 01 Mar 2023 21:35:49 +0000

Court transcription is an integral part of the legal process. Court reporters are responsible for maintaining accurate records, making sure each person is properly sworn in and introduced during a deposition.

The court reporter is the guardian of the record and is responsible for accurate and complete information when transcribing court proceedings. They perform an essential duty. According to the Court Reporter Statute, 28 U.S. Code § 753:

“Each session of the court and every other proceeding designated by rule or order of the court or by one of the judges shall be recorded verbatim by shorthand, mechanical means, electronic sound recording, or any other method, subject to regulations promulgated by the Judicial Conference and subject to the discretion and approval of the judge.”

Court reporters provide a vital service, but unfortunately, there is a severe shortage of them coming into the industry, and that scarcity is causing a crisis in the legal industry.

Nationwide Court Reporting Crisis

The United States is facing a major crisis in the legal industry – with more court reporters leaving than joining the field, there are fewer people to play that vital role in the court system. In the state of California a group of legal officials declared the court reporter shortage a crisis with serious concerns about the effects of the shortage. In fact, according to Brandon Riley, the San Joaquin Superior Court Officer, of the 170 people who took the exam last year in California, only 36 passed, which is a serious issue when levied against the need for 2700 total court reporters across the state!

If actions are not taken, lawmakers fear this bottleneck will further slow down the legal system and cause serious consequences.

As a result, this issue is being raised to the status of a crisis and being reported by courts across the United State of America. The coalition of legal officials raising this issue in California stated “the legal system needs to embrace modern alternatives” and updates to laws limiting the use of reliable technologies such as electronic court transcription.

Technology Solutions On The Rise

In order to meet the demand for court reporting, officials are suggesting the use of technology such as transcription services to support both court reporters and digital reporters in providing transcripts. Transcription companies such as TranscribeMe are paving the way forward through this crisis.

Our transcription service offerings are perfect for helping to ease this court reporting crisis, as we are able to provide the rough drafts and scoped transcriptions of depositions, law enforcement interviews, and other important court proceedings, which can then be quickly proofed by the court reporters. This frees up a significant amount of the court reporter’s time since they don’t have to focus on creating the transcripts. With the extra time, reporters can take on a greater volume of proceedings. If legal firms and court reporting agencies quickly adopt and use our services, we can take some much-needed pressure off of them and the legal system as a whole.

By using our transcription services, court reporters can greatly increase their efficiency by focusing on proofing transcripts and ensuring their accuracy, rather than manually transcribing court documents live, and going through all of the remaining steps. With the ability to schedule more proceedings for each court reporter, firms can significantly increase their revenue.

Benefits of Transcription Services for Court Reporters

Saving time

Court reporters take on average 4-5 times the length of a recording to fully transcribe it, meaning a 3-hour long deposition could take almost two full working days to process! By utilizing TranscribeMe, the reporter would have that time available to add more proceedings to their calendar.

Proper formatting

TranscribeMe can make sure that transcriptions fit any type of legal formatting, and can work with you to create custom formatting to match the needs of a particular jurisdiction.

Potential for more revenue

The use of court reporting transcription services is an investment. While it does cost money to utilize outsourced transcription services, firms are experiencing greater efficiency and profit by freeing up their court reporters' time to handle many more cases.

At TranscribeMe, our top priority is creating high-quality transcriptions in order to bring our clients ease. While law firms and legal agencies cannot control the court reporter shortage, they can take matters into their own hands by using transcription services designed directly for their needs. We would love to help you, so please contact us at 1-800-275-5513.

Interested in learning more? Get a FREE quote today.

The post The U.S. Court Reporter Shortage Creates A Need For Transcription Services appeared first on TranscribeMe.

How to Pick the Best Medical Transcription Service

Transcribe Me — Thu, 22 Dec 2022 01:16:53 +0000

Choosing a medical transcription service that you can trust provides enormous benefit to your practice or organization. Companies that specialize in delivering speech-to-text transcriptions provide significant cost savings over creating transcripts in-house, and give you the ability to focus on your core business rather than investing valuable time and energy on hiring and managing a team of transcriptionists. Outsourcing to a competent medical transcription provider will save time and money while freeing up administrative resources.

In the medical space, it is important to be cautious when outsourcing transcription since you are likely dealing with sensitive data that may contain personal health information (PHI). Finding the right transcription vendor can be tricky, but this guide will help you understand what to look for in a provider and how to make sure they are compliant with standards from the Health Insurance Portability and Accountability Act of 1996 (HIPAA).

Table of Contents

Issues with doing in-house and why outsourcing is better
Top Questions to ask potential vendors
Why TranscribeMe works best

Benefits of Using a Medical Transcription Provider vs Doing Transcripts In-House

At first glance you would think that utilizing in-house staff to take care of your transcription needs would be a more secure and efficient way to generate and maintain your transcripts. This is wrong. The reality is that generating transcriptions in-house is less-secure, more expensive, and utilizes greater resources versus outsourcing the work to a professional transcription provider.

Security Issues

Maintaining proper HIPAA compliance across all the processes within your organization is difficult and there are many instances where violations may occur. This is especially true with transcription. If your in-house transcriptionists are not using specialized software dedicated to transcribe data containing PHI, or are not properly trained in handling and processing audio/text data containing PHI, it is easy to have user errors or lapses in judgment that result in HIPAA violations. Likewise, if you do not set up your end-to-end technology infrastructure in a way that ensures HIPAA compliance, you are likely opening yourself up to violations.

Shockingly, HIPAA violations from covered entities happen fairly frequently, according to the Department of Health and Human Services (HHS). However, this data is just from what was reported. It wouldn’t be surprising if many more unreported HIPAA violations occur every year due to poorly managed internal processes or employees covering up mistakes, or from organizations that don’t want to risk a fine for committing these violations.

Credible transcription companies, on the other hand, have dedicated workflows and processes in place to ensure HIPAA compliance every step of the way. The firms dedicated to HIPAA compliance will build their technology infrastructure around a variety of methods that ensure the highest level of information security, including the following:

End-to-end encryption of data in-transit and at rest
Limited access to data containing PHI only to appropriate users
Deleting data from servers after a certain period of time
Careful vetting of workers and employees that have access to sensitive data
A culture and experience in working with PHI data

Companies that dedicate themselves to HIPAA compliance will prioritize their R&D and deployment efforts to maintain this compliance, and with the appropriate legal contracts in place will ensure that your data is more secure than performing these services in-house.

Cost Issues & Productivity Losses

There are two ways for medical transcripts to be generated in-house; either with internal staff doing the work themselves, or dedicated transcriptionists/medical scribes. Transcription is a skill set that anyone can do, but requires extensive training and experience to do well and be efficient.

Professional medical transcriptionists or scribes will be effective and deliver accurate transcripts in a timely manner. The drawback is that these folks also typically command very expensive per hour rates, and will also increase your employee headcount which will also increase your fixed costs. The cost difference will likely be several orders of magnitude higher to bring professional transcriptionists in-house and you will not see significantly better productivity or accuracy.

By outsourcing to a medical transcription service, you will receive a menu of pricing options that are typically billed at a per audio minute rate. Due to economies-of-scale, transcription services will generally be able to offer pricing that will come out to 50%-80% more affordable than in-house transcriptionists. They will be able to handle any volume and most charge only for the amount of minutes ordered. Internal staff will cost you the same amount every month and there is a limit to how much work they can do without incurring additional costs for overtime pay. A transcription provider can scale up or down as needed without incurring these additional fees. It just makes more sense financially to outsource this work to an appropriate vendor.

Efficiency & Accuracy

Staff not trained or experienced in performing transcription work will take significant time and energy to create proper transcripts, keeping them from doing other valuable work. Additionally, while transcribing, it is difficult to maintain focus and if your staff is not experienced or does not enjoy the work, they will burn out quickly.

Companies that specialize in delivering medical transcription services have dedicated sourcing, training, onboarding, and maintenance of workers and are able to source transcriptionists from a wide variety of locations.This provides an advantage in that these companies can work with you to deliver exactly what you need when you need it.

Most of these companies also have specialized transcription platforms allowing them to return transcripts faster and more efficiently while maintaining control over the data and ensuring the highest possible quality.

Why the TranscribeMe Medical Transcription Service is the Best

At TranscribeMe, we strive to provide the highest quality medical transcription, delivered quickly and at the lowest cost. We have built a proprietary workforce management platform that combines the latest in speech recognition technology paired with the best in human intelligence. This enables us to deliver HIPAA compliant workflows with the industry’s highest information security protocols and standards.

Additionally, we primarily source our workforce in North America and have a robust training, QA, and career path for workers. This allows us to create incentive structures for workers to deliver consistent high quality output. We work with most major hospital networks and universities that require HIPAA compliant transcription processes and know what it takes to deliver the best quality data at scale.

We are also transparent in our processes, flexible in how we can set up teams of workers to facilitate your needs, and ensure that only the best quality transcriptionists are assigned to work on your data. Please contact us to learn more.

Lastly, our biggest priority is the safety and the security of your data. Data is stored and maintained in the United States, workers that have access to your data must sign a BAA, and we even have a standard practice of deleting your data within 30 days upon project completion as an added layer of security.

The post How to Pick the Best Medical Transcription Service appeared first on TranscribeMe.

HIPAA Compliance for Transcriptions, What You Need to Know

Transcribe Me — Tue, 22 Nov 2022 18:03:24 +0000

Just about everyone has heard of HIPAA in one form or another. But what exactly is HIPAA compliance, and how does it relate to transcription companies and their services? To delve further into what requires HIPAA Compliance, we first need to clarify the difference between different categories of data and which ones fall under HIPAA requirements.

Data that pertains to the Healthcare field can take the form of medical practitioner’s notes, patient interviews, medical research reports, case study summaries, drug trial subject interviews, medical conferences, etc, but not all of it necessarily requires HIPAA compliance.

A Brief Overview of HIPAA Compliance

The Health Insurance Portability and Accountability Act of 1996, commonly known as HIPAA, is a series of USA-based regulatory standards that outline the lawful use and disclosure of Protected Health Information (PHI). This series of regulations have become a standard for many countries across the world.

This compliance is regulated by the Department of Health and Human Services (HHS) and enforced by the Office for Civil Rights (OCR) in the United States. The OCR’s role in maintaining medical HIPAA compliance comes in the form of routine guidance on new issues affecting health care and in investigating common HIPAA violations.

HIPAA regulation identifies two types of organizations that must be HIPAA compliant:

Covered Entities: Which is any healthcare organization that collects, creates, or transmits PHI electronically.
- Healthcare organizations that are considered covered entities include healthcare providers, healthcare clearinghouses, and health insurance providers.
Business Associates: Business associates are defined by HIPAA regulations as any company or organization that encounters Protected Health Information (PHI) in any way while working on behalf of a covered entity. These include transcription companies as well.

The HIPAA Rules that Apply to Transcription Companies

The HIPAA Rules that pertain to using a third-party transcription company include:

HIPAA Privacy Rule: The core takeaway from this rule is that a provider’s regulatory standards must be documented in the covered entities’ policies and procedures. For medical providers to use a transcription company’s compliant service, the provider must impose specified written safeguards on the information used or disclosed by its business associates. This is outlined within a Business Associates Agreement (BAA).

HIPAA Security Rule: This states that the covered entity (or healthcare provider) must make sure that they have met the national standards for the secure maintenance, transmission, and handling of PHI data. It’s crucial that covered entities understand that liability does not fall on the Business Associate, it falls on the covered entity, and that’s why it’s important to verify the process is being followed.

TranscribeMe provides secure maintenance, transmission, and handling of ePHI through our SFTP and cloud based systems which restrict access to data to only those people who are qualified and trained on HIPAA compliant procedures and practices.

HIPAA Omnibus Rule: This states that the Business Associates must comply with the security rule.If a transcription company is saying ‘we’re HIPAA compliant’ but does not sign a BAA, then they are not HIPAA compliant.

Not all Medical Data Needs to be HIPAA-Compliant

Very often, our teams are requested to provide a HIPAA-compliant workflow but come to find out that they do not need that type of workflow. Some common examples that we see a request for unnecessary HIPAA-compliant workflows are:

At a medical conference, where the participants are discussing general medical research or results, but patient names or any Protected Health Information (PHI) is not referenced, then that type of information does not need to be handled in a HIPAA compliant workflow.
Focus group discussions where there is no identifiable information revealed. If eight people are in a focus group and their names are not used, and the topic of the focus group is “how are you faring six months after chemo?” if they use the participant’s names in publications, then yes, a HIPAA-compliant workflow should be used.
However, if participants are anonymous, then a compliant workflow isn’t necessary.
If the participant mentions they are taking this medication and receiving these treatments, then yes, that is Protected Health Information (PHI) and would require a HIPAA-compliant workflow.
Any medical information that’s discussed in general terms, does not require a HIPAA-compliant workflow – it’s when you get into a patient’s specific information, that’s when a HIPAA workflow is necessary.

Most patient studies and research studies all tend to need HIPAA compliance.

Not all HIPAA-compliant data is related to the medical field

A common misconception about HIPAA is that it is only related to the medical field when in reality HIPAA compliance comes up across many industries – here are some examples:

Data research firms will sometimes have interviewees that give Personal Identifiable Information (PII) and discuss health-related information. Once that happens and is recorded, that data research firm must make sure that their audio is handled through a HIPAA-compliant workflow.
Market research firms handing out surveys usually contain PII and must be handled through a HIPAA-compliant process.
1. For example, a medical research firm asked the general public ‘have you gotten headaches before?’ or ‘what have you done to get rid of your headaches?’, along with ‘what’s your first and last name’
Law firms specializing in personal injury, insurance defense, malpractice, and elder law are likely privy to client protected health information (PHI). However, attorneys working in other practice areas may also deal with PHI, so all attorneys must follow the security and data privacy standards set forth by HIPAA guidelines as well.

Hospitals for the most part have to be completely HIPAA compliant since their business is in treating patients.

HIPAA Compliance Needs Will Depend on the Data Stored

Overall, whether or not you need to use a HIPAA-compliant workflow depends on what is in your data, and not the type of data that you have. The responsibility and liability of ensuring that your data is transcribed using a HIPAA-compliant workflow falls on the covered entity, not the 3rd party.

One last important aspect is that If a covered entity receives written consent from the patient to use their data, then HIPAA compliance no longer applies. It’s all about privacy and the patient’s control over their privacy.

TranscribeMe takes HIPAA compliance seriously and has the experience to provide accurate and compliant medical transcription services. Our HIPAA-compliant transcription services will provide you with accurate and timely service.

The post HIPAA Compliance for Transcriptions, What You Need to Know appeared first on TranscribeMe.

2 Major Benefits of Professional Medical Transcription

Transcribe Me — Sat, 10 Sep 2022 02:23:27 +0000

Professionally executed, high-quality medical transcription is vital to healthcare providers and practitioners for a variety of reasons, but is difficult to source and have confidence with the final product. Organizations frequently have debates about whether to use in-house transcriptionists or to outsource the data to transcription companies and weigh the differences in costs, delivery capabilities, and accuracy.

The scope of this article will cover the ABCs in how organizations will see great benefits from using a professional medical transcription company like TranscribeMe, which are: Accuracy and Burnout.

Table of Contents

Ensuring Medical Record Accuracy
Relieving Physician Burnout Maintaining Electronic Health Records

1. Ensuring Medical Record Accuracy

Keeping accurate medical records is a crucial requirement for healthcare providers on both the legal and professional front. Without accurate and professional printed or electronic medical records, including records on personal information and data, physical and mental conditions, medical history, and current medical care, there is increased risk for patients to be improperly or outright mistreated.

Sheila Burgess, RN, RHIA, CDIP, CHTS-CP, director of CDI at Sutherland Global Healthcare Solutions, points out that even a small typo such as “hyper” versus “hypo” in a medical record can have dire consequences. “If a patient comes into the ER unconscious, they could be emergently treated for a condition they don’t have if a physician pulls up their record and the documentation is incorrect or it’s the wrong patient’s information,” Burgess says. “That’s one thing that could be detrimental.”

On the legal front, the Federal False Claims Act requires that patient medical records be kept for 10 years. All medical procedures, treatments, care, and instructions must be documented, and all entries made are legally binding. Improper, inaccurate, or poorly managed documentation may result in serious HIPAA violations which can result in fines and a loss of reputation. Additionally, further legal, civil, or professional liability may be incurred if patients are mis-diagnosed or mistreated stemming from poorly maintained or inaccurate medical records.

At TranscribeMe, we have built a platform around a focus to deliver consistent, high quality data. For medical transcription, a robust workflow that requires medical transcriptionists to demonstrate competency with medical terminology, language, and general grammar and dialogue in order to receive access to transcription tasks.

Additionally, multiple layers of review are built in to make sure that workers are constantly checked and that data is accurately transcribed with consistency. This results in a process that delivers consistent high quality data affordably and quickly.

TranscribeMe’s medical transcription service teams are able to provide customized outputs for special writing styles or proprietary formats required by health providers regarding the transcription of their medical records.

As a company with a dedicated medical transcription division, TranscribeMe can retain such requirements in portfolios according to individual customers, and the medical teams know which styles are to be used for which providers. Sometimes the same healthcare providers may even have several different style requirements according to format: letters, diagnoses, reports, etc. The final output can be customized to your needs.

According to Health Information Management (HIM) experts, the most common errors made in medical documentation are mixed messages from a physician via misunderstood dictation or illegible handwriting; misuse of copy-paste functions in the electronic health record (EHR); incomplete or missing documentation; misplaced documentation. Errors such as these can be mitigated by the use of a trained, experienced medical transcription team.

2. Relieving Physician Burnout Maintaining Electronic Health Records

The results of two surveys conducted in 2013 and 2015 by Medscape showed the same list of causes that lead to physician burnout. The top cause was revealed as too many bureaucratic tasks, and the second as too many hours at work. Surprisingly, the fourth was increasing computerization, a factor that should seemingly help with the bureaucratic tasks and decrease work time instead of hindering them.

In a 2014 Medscape survey, 70% of physicians noted that the implementation of EHR in their practices decreased face-to-face time with patients, and 57% said that it detracted from their ability to see patients. The survey did show indications of it becoming less problematic in the future – 81% of the physicians surveyed said they are becoming more comfortable with their EHRs over time – but being more comfortable with computerization does not necessarily mean solving issues that cause burnout.

According to Medical Economics magazine’s 3rd annual Physician Burnout and Wellness Survey in 2021, physician burnout has reached a crisis point. When asked what contributed the most to their feelings of burnout, the number one answer given by physicians was too much paperwork and regulations, followed by work too many hours/poor work-life balance. These results are directly in line with the survey results obtained by Medscape seven and nine years prior, respectively.

In his article We Must Address the Causes of Burnout, L. Allen Dobson Jr., MD, FAAFP, the editor-in-chief of Medical Economics, describes how in the early years of his practice the causes of physician burnout were most often attributed to long hours, lack of work-life balance, the pressure of clinical responsibility, and the burdens of call and leadership. “Now the most cited reason in numerous studies and surveys is administrative burden (EHRs, prior approval, administrative tasks).”

Utilizing the Services of a Medical Transcription Company

By utilizing a transcription company such as TranscribeMe, providers can allocate more time to duties of greater urgency and have the peace of mind knowing their transcription needs are taken care of. Healthcare providers can simply use a recording device or mobile phone to record diagnoses, treatments, reports, schedules, and more, instead of typing out the records themselves. In this way, important medical information will be captured with minimum effort and the recordings can be forwarded for transcription by professionals of the field.

Not only is this beneficial to the physician due to medical records becoming a no-hassle process that will help to alleviate the main causes of physician burnout, patients will benefit as well because records and transcripts will be implemented quickly and professionally.

In addition, the healthcare provider can also be under less pressure in knowing that medical transcripts will ensure that key data and metrics are available in a timely manner to healthcare insurers for proper claims handling. Outsourcing transcription can also help to free up the administrative personnel in a physician’s office for higher priority tasks, thereby helping to alleviate staff burnout as well.

HIPAA-Compliant Transcription

Confidentiality requirements and the protection of personal data continue to increase as a major priority across the globe. Many countries have implemented laws and regulations that require the protection of certain types of data by anyone who is in possession of this data. However, not all medical data requires this type of protection, and not all data that does require this protection is medical data.

The Health Insurance Portability and Accountability Act (HIPAA) was created to “improve the portability and accountability of health insurance coverage” for employees between jobs. After it was signed into law on August 21, 1996, the US Department of Health and Human Services began creating the first HIPAA Privacy and Security Rules such as the HIPAA Privacy Rule, which came into force April 14, 2003, and the HIPAA Security Rule, which went into effect on April 21, 2005. HIPAA and its related regulations have since become an informally accepted global standard.

HIPAA requires that healthcare facilities and providers, as well as anyone with access to Protected Health Information (PHI), take serious measures to ensure the privacy and security of personal patient data. Healthcare professionals must have firm security precautions in place or face dire legal consequences.

A HIPAA-compliant transcription service such as TranscribeMe comes with multi-layer security features. Security protocols include encrypting data in-transit and at rest, limiting access to records for only essential personnel, deleting data off of our servers after a short period of time, and many others to ensure compliance is met.

Office administrative staff in a doctor’s office or clinic may find it difficult to keep up with all the complexities and details of HIPAA compliance. TranscribeMe employs staff that is specifically dedicated to meeting HIPAA requirements and restrictions. This staff continuously educate themselves on the regulatory updates, make company administrators aware of the latest developments, and write company policies, guides, and informational material to ensure that our process remains truly HIPAA compliant

Furthermore, TranscribeMe keeps control of and tracks all who have access to sensitive data. Individuals on the HIPAA medical transcription team must pass through several levels of identity verification. They are also required to sign a non-disclosure agreement (NDA), a business associate agreement (BAA), pass a HIPAA compliance exam annually, and fill out a Transcription Security Declaration form after each project verifying no HIPAA-related material was improperly copied or taken.

Increasing quality (Accuracy) and eliminating barriers to productivity (Burnout) may be the main driving force for choosing TranscribeMe to process medical records, but guaranteeing the safety of personally identifying information in doctor-patient exchanges (Compliance) remains paramount. No matter which of these specific ABCs takes priority in your practice or facility, TranscribeMe can help take the worry out of handling medical data.

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The Affordable, Easy Way to Transcribe Audio to Text

Transcribe Me — Wed, 31 Aug 2022 09:02:59 +0000

If you’ve ever had a need to transcribe audio to text, you’ll likely love TranscribeMe’s transcription tool.

For business professionals, students, media experts, market researchers, legal / medical professionals, and many others that conduct regular meetings, interviews, and record other kinds of potentially valuable audio/video, converting audio to text can save heaps of time and energy.

Why Transcribe Audio to Text?

More efficient and effective than writing by hand, converting audio to text is a powerful tool that can benefit users. There are a number of reasons why professionals and time-savers love to use our transcription services:

It’s easier than typing notes out manually.
It’s available anywhere on-the-go.
It’s notorious for getting all the details.
It’s a simple way to search through dialogue.
It’s a proven tool for saving time and money.
It creates a digital index of information for fast recall.

Need we say more? Our audio to text service is incredibly accurate and reliable, boasting a hybrid transcription process. With this method to convert audio to text, speech recognition technology is first used and then expert transcriptionists come in to ensure perfected text files.

TransribeMe Transcription Services

At TranscribeMe, we offer a suite of different transcription services that can fit any kind of use case. The high-quality human annotated transcription options we provide include:

Additionally, you can opt to purchase a 100% machine speech recognition transcription at only $0.07 per audio minute. This option delivers a lightning fast output that will have pretty high accuracy with good quality recorded audio. Each of these transcription services also has an upgrade option where you will only be charged the difference if you would like a higher level of service and additional review by our human quality assurance experts.

Audio to Text in 4 Simple Steps

After you set out either a recorder or utilize our free transcription app to capture the audio you want to transcribe into text, it’s a pretty simple process. Depending on the file size, audio quality and turnaround time you’ve selected, you can have your transcription voice to text in your hands in a matter of minutes or days.

Log in to your account

You’ll see the main homepage that allows you to upload audio/video content. You can also organize your content into folders, and download completed transcripts in a range of formats.

Upload your file

Click the “Select files from my computer” button and choosing the file you wish to submit; you’ll see a real-time upload status to see where your file is during the process.

Order your transcript.

Once the file is uploaded, click “Transcribe.” This will take you to the Order Details page, showing you the total price and details that can be edited.

Receive your audio-to-text file!

After verifying payment and other details, and confirming your order, you’ll receive your finished transcripts within the time specified.

The process is simple and takes very little time so you can focus on other things. You will be notified when your order is completed and ready for download.

Affordable Audio to Text Transcription

Have a limited budget? Our first draft transcription service is the most affordable transcription solution anywhere, making it much more possible to get things done. With audio to text starting at $0.79 per minute, our low-cost transcription service:

Offers fast turnaround times (typically 2-3 business days, and often within 24 hours).
Features 90-95% voice-to-text accuracy, based on audio quality received.
Is perfect for everyone from students to busy professionals!

Looking for something a little closer to 100% perfect transcription? Our standard transcription service is still a cost-effective way to convert audio to text and provides specialized transcription services. Our review process consists of many levels of review to ensure the highest level of quality, and we guarantee 99%+ accuracy with every project.

So as you can see, TranscribeMe’s audio to text transcription services are perfect for professionals looking to convert audio to text from interviews, videos, phone calls, audio notes, lectures, speeches, podcasts, meetings or really any recorded speech to text in a number of different languages. Export your transcript as doc, txt or subtitle files. Want to learn more information about TranscribeMe’s audio to text transcription services? Feel free to reach out to one of our representative today!

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Evaluating Automatic Speech Recognition Technology

Transcribe Me — Thu, 07 Jul 2022 23:28:31 +0000

Let’s talk about Automatic Speech Recognition (ASR) technology: the state of the art; user/customer expectations; ASR output vs user/customer expectations; and ask whether there is one ASR engine that meets all requirements? Spoiler alert–the answer to that last question is, no. There’s no single ASR Engine that can satisfy all industry needs. Why not? We will dive into that answer in a bit.

Here’s another question to ask about ASR Technology. Why should I, the consumer, look beyond the big three–Google, Apple, Microsoft…make that four, IBM to assist me in meeting all my ASR Requirements? Obviously they have the biggest R&D budgets and attract the best talent so their technology should be the best, right?

The answer is, it depends.

ASR Technology Hits and Misses

For example, you want Google to turn on the lights–”Google! Turn on the driveway lights.” Or, “Siri! Play my, I’m really depressed mix.” Or “Alexa, I need a Vegan pizza, light pepperoni and cheese.” All of these technologies that use ASR to pick up on voice commands work pretty well.

However, there are a number of cases where these ASR technologies have challenges. One pretty simple example is when I use the speech to text feature on a phone. Between auto correct and incorrect words, it’s definitely not perfect. In fact, what is most frustrating is that it doesn’t learn. I always have to correct my daughters’ names as well as that of my engineering VP–EVERY TIME! This is a slightly different use case than query response, but it’s similar. Typically short sentences, real time transcription and the errors are because the context is free form so there isn’t the possibility of comprehensive training.

How TranscribeMe Uses ASR

The TranscribeMe use case for ASR is neither of these. Eg, “Ok Google! Listen to this one hour audio file and transcribe it with timestamps for every speaker change.” As they say colloquially, “that dog don’t hunt.” Why not? That’s not the use case for Google.

Simplistically, the ASR industry breaks down into two use cases, query/response and audio to text transcription. TranscribeMe continually tests vendors’ speech engines and the big 3 or 4 are never at the top of the list in terms of word error rate for our use case–and that makes sense–audio to text, where audio, not ‘spoken speech’ is not their design target.

An example of a TranscribeMe virtual request might be, “Transcribe this six hour legal deposition with five speakers using the state of Iowa output format and include speaker IDs and speaker change timestamps.” Well, truth time, no ASR engine is going to get that right. But some may be better than others.

So that’s where ASR analysis becomes more sophisticated. We’re not simply looking at word error rates but at other factors, such as which engine punctuates or capitalizes best? Which works best w/ crosstalk? Which is stellar with single speaker or multichannel vs that which can handle multiple speakers on a single channel?

Why do these qualifications matter? Because the speech engine is not going to produce the final output that will be acceptable to the customer. Maybe it will produce output that’s 90% correct–that’s pretty good. What if your car worked 90% of the time–pretty good or totally unacceptable?

No ASR Engine’s Output is Perfect

No ASR engine with a few caveats can produce output that will be acceptable to the customer as a finished product. The ASR engine produces an output that then requires human review and correction for completion. And that human in the loop dictates which engine we use for various customer and use cases–those distinctions I mentioned above: dial up the ASR that excels at single speaker clear audio; or dial up the ASR that accurately timestamps speaker changes; or we need the engine that doesn’t insert gibberish when it doesn’t understand the audio.

In summary, the TranscribeMe use case requires different engines for different types/qualities of audio and for specific use cases. Since we don’t build our own ASR we can shop and use any vendor that fits our needs and provides the best output for human review and correction.

I mentioned a caveat where there are cases that a one pass ASR output can satisfy customer requirements and in our case we have a customer who does further analytics on the ASR output. That analysis may be keyword spotting or sentiment analysis, or other.

As an aside, be wary of any company using their own home grown ASR to process files–one size does not fit all and companies that do produce their own ASRs continually narrow the niches where they play.

Do you have some examples of ways where you have found ASR technology challenging with a project you have worked on? We’d love to know. Are you looking for a company like TranscribeMe to help you with any of your Transcription or AI Datasets and Machine Learning needs?

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How Does Speech Recognition Work Exactly?

Transcribe Me — Fri, 28 Feb 2020 16:54:10 +0000

Today’s fast-paced lifestyle, combined with a growing preference for finding simplified ways of completing daily tasks and responsibilities, has led to the proliferation of the use of speech recognition.

Indeed, since Google’s introduction of voice search in 2011, which was then considered a novelty, the feature is now one that users regularly rely on. What’s more, improvements in speech recognition technology have transformed voice search into a key component of search marketing.

Within the realm of artificial intelligence, voice recognition is completely transforming the way that we interact with technology. And now, with the availability of smart-home voice assistants (Alexa, Google Assistant and Siri) that undergo regular updates to their software which continue to improve their intuitiveness and intelligence, voice recognition is a part of regular daily life for many.

The accuracy and complexity of speech recognition technology makes one wonder, what is really going on under the hood? How does speech recognition work? Below we delve deeper into understanding it.

How does it all work?

Speech recognition technology comes in a few forms; in some cases, it serves as an alternative to typing on a keyboard; words appear on a screen by way of talking to the computer thanks to software that analyzes the audio of a speech recording using algorithms to accurately match the individual sounds to written language.

In other cases, speech recognition technology translates audio algorithmically into a certain action that is then performed by another piece of technology — as is the case with smart-home assistants, which translate users’ speech to commands like turning smart devices on our off, or changing the song that is currently playing.

Whatever the end goal might be, speech recognition technology works very similarly in the aforementioned situations; an audio message — whether on your phone or desktop — is transcribed on the server. The bits of data from the audio message are sent to a central server, where it can access the appropriate software and corresponding database. Here, the server analyzes the audio and breaks down the speech into smaller, recognizable parts called phonemes. From here, it’s the phonemes that enable audio analysis software to figure out exactly what is being said. In the case of words that are pronounced similarly, the software is able to analyze the context of the audio and syntax of the sentence to identify the best text match for the words within the audio file.

Finally, this analysis results in a written transcription of the data or in a secondary action in the form of instructions to be undertaken by another piece of technology (like a smart-home device, for example).

At TranscribeMe, we use our Machine Express service for speech-to-text transcription. It employs the most advanced automated speech recognition algorithms to create the highest accuracy automated transcriptions on the market.

If you would like to learn more about the product or have bulk or custom requirements, contact our Sales Team today!

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How to Transcribe Your Videos in 3 Easy Steps

Transcribe Me — Mon, 10 Feb 2020 14:42:13 +0000

At this day in age, transcribing your videos could not be any easier, thanks to the wide range of online tools available. Many of them fall short however, when it comes to the quality and accuracy of their offerings. At TranscribeMe, the accuracy average of our transcription services starts at approximately 98%, and you can expect your transcript to be delivered to you within 1 to 3 business days. Getting started with us is fairly simple too, with the help of our straightforward sign up process; with just a few clicks, you’re all set to upload your video files.

Keep reading below to see how easy it is to transcribe your videos with us!

1. Sign Up for a TranscribeMe Account

Our sign up process is simple, and we wanted to keep it that way! With just a few clicks, you’re ready to upload and store video files to your TranscribeMe account. Our dashboard makes it easy to organize files into folders, and filter them by name, status, start date and end date.

You’re also able to view and sort video files on the TranscribeMe app, but keep in mind that for the time being, video file uploads can only be done through the TranscribeMe desktop application.

2. Select the File that You Wish to Transcribe

Our general rule of thumb is that when it comes to uploading video files, if you can play a media file on your computer, we should be able to transcribe it; this includes mp4, mov, avi, flv, 3gp, 3gpp, 3ga, amr, caf, m4v and mxf files.

With the help of the TranscribeMe desktop application, you can upload video files stored on your computer or your Dropbox account. You can also provide a direct web link for even faster uploading.

If you have any questions regarding which file formats we support or how to go about uploading your video files, don’t hesitate to reach out! We’d be happy to answer your questions.

3. Download Your Transcript

Once you’ve submitted a file to be transcribed, you can expect to receive your transcript within 1 to 3 business days, depending on the transcription service elected.

When your transcript is ready for download, you will receive an email notification that your transcript is completed. To download a transcript, navigate to the Customer Portal Account, where you will be able to choose between Word, PDF and HTML formats for secure download. TranscribeMe app users can view transcription text in the app by clicking on the filename in the list of recordings.

TranscribeMe is the perfect choice for your video transcription needs. We use the most advanced technology in the industry to provide expert transcribers with best-in-class tools to provide exceptional results at the lowest prices. Our proven track record of customer satisfaction is combined with our tireless passion to provide the highest quality of transcription services.

Contact our sales team today to request a demo and see exactly what our unparalleled transcription services can do for you, or upload your video directly to the TranscribeMe portal today to receive your fast and accurate transcript.

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