Hipaa Compliance: Fill & Download for Free

There are a number of common complaints about HIPAA provisions. I'll summarize some of the main ones:Parental access to their adult children's informationOnce children reach the age of 18, parents are no longer eligible to access their medical information. This is true even if the children are still on the parents' insurance policies, making the parents guarantors for payment of their children's procedures. That is, a parent could easily be in the position of being asked to pay for an adult child's procedure, but not able to ask the doctor's office for any details of what was done.Difficulty of doing medical researchHIPAA compliance makes it much harder to do retrospective studies. My wife actually helped design and perform a retrospective study while getting her Pharm.D., and a substantial chunk of the budget went to having the medical records department redact records to make them HIPAA-compliant.Prospective studies suffer too. Under HIPAA, informed consent forms have to include a large privacy section filled with legalese. One study that implemented HIPAA-compliant protocols suffered "...a 72.9% decrease in patient accrual, and a threefold increase in mean personnel time spent recruiting and mean recruitment costs." (http://www.ncbi.nlm.nih.gov/pubmed/16342254 )Over-caution by providersAs with any incredibly complicated law, most of the people affected by it don't fully understand all of the provisions. This leads to all sorts of knee-jerk overprotective policies. E.g., during a hospital stay the hospital placed all sorts of barriers in the way of my wife seeing my chart, blaming "privacy laws" that had no basis in HIPAA. The atmosphere of fear can impede communications, especially in situations where the patients ability to communicate or monitor their own care is impeded.Cost, waste, and bureaucracyHIPAA generates a lot of paperwork that few people ever read. Notices of privacy practices get printed in bulk. Fortunately, the costs are much less than they were back when HIPAA was introduced; all medical information systems now include HIPAA features out of the box. However, the cost to train personnel on HIPAA requirements remains a major expense, and additional personnel (or, for a small practice, a portion of a person) is necessary to perform compliance duties.IneffectivenessWhile I agree with the general privacy intent of HIPAA, the implementation isn't particularly effective. Individuals cannot sue under HIPAA for privacy breaches (although they can often sue under state privacy laws or common law principles such as negligence). All an individual can do is file a complaint with the Department of Health and Human Services, which will either be ignored or be added onto a backlog so long that it might as well be. Just last year, University of California at Los Angeles Health System settled charges that employees were browsing through patient records (http://www.hhs.gov/news/press/2011pres/07/20110707a.html). Actually doing as DHHS recommends, and having someone review all audit trails to catch unauthorized viewing, would dramatically increase the cost of HIPAA compliance.ConclusionI need to point out that most of these problems could be addressed with amendments to HIPAA. In many ways, a repeal would be a big step backwards - I think most Americans agree that they should have privacy protections, especially as their medical information migrates from paper charts to networked databases. But compliance could be made much easier, and allowing private lawsuits against employees such as those at UCLAHS that deliberately violate confidentiality would give the law more teeth. I don't think that most politicians would find a repeal of HIPAA, as opposed to a reform, politically palatable.

Safety data, such as labs (which may encompass any body fluid people produce, depending on the study), ECGs, physical exam results, vital signs, hearing tests, vision tests, neurological exams, pulmonary function tests and so forth, depending upon the disease/disorder in question; some of these tests may be done because of adverse events discovered during animal testing or adverse events of similar medications.Should a subject be hospitalized, become pregnant, develop cancer, or have some other "medically important" event, we gather all information related to the event (whether it is possibly related to the study or not) and report to the sponsor and our institutional review board within 24 hours of the researcher or other study personnel learning of the event.Efficacy data may include physical exam results, quality of life tests, clinician administered tests (particularly in psychiatric studies), and comparison of any safety data (see above) collected at the first visit.We also collect such things as study drug compliance, previous medical history, current and past medications, adverse events that occur during the study (whether they have anything to do with the drug or not...if someone attacks you at a bar and blacks your eye when you are in a study, that injury will be noted).In addition to data collected on patients, we collect data on the researchers involved at our site AND the study itself. There's study correspondence, medical licenses of any study personnel, duties of study personnel are noted, any laboratory certifications, continuing education (as regards to the study), information on the protocol, consent forms, HIPAA forms and other study documents (and any changes to those), paperwork for any errors we make -- such as errors in obtaining consent (have not made this one in 20+ years of doing this), subject non-compliance, having study visits out of window, medication errors (haven't done this one either, although sometimes my patients do), doing visit procedures out of order, loss of study drug by a subject...the list goes on and some of these I have no control over, but still have to report.

Innovations and significant changes come from an idea. The same goes for chatbots. When starting a chatbot development process, keep your initial idea in mind, then you have to shape it into a plan with requirements. Afterward, you can start prototyping and testing your chatbot.Let’s break the development process into 4 steps:Step #1. Research and analyzeThe first thing when developing a chatbot is to do research and analysis. You need to highlight the reason for implementing a chatbot. This will help you to understand better what problem the chatbot will be tackling. This is a very important part and to help you define a way of understanding and moving forward, we’d recommend you to answer a few questions:– “Which problem would it solve?”– “Why are you developing a chatbot?”– “What will exactly a chatbot be doing?”It is necessary to understand that a chatbot must directly solve the end-user problem or optimize processes inside the company. The main advice here is not to overload your bot with additional and unnecessary features and to focus on one specific problem.Step #2. Find a user persona and create a chatbot personalityIt’s important to think about user persona because based on that you will build bot’s personality. The personality of the chatbot is the way how a user imagines a bot during a messaging with it. A chatbot can be a friend to users providing them with assistance and helpful advice. It can be as different as you wish – serious and formal, witty and adventurous or careful and professional. It’s up to you to choose your chatbot personality.Could you imagine how your product would be portrayed as? Would it be male or female? Is it young or perhaps a bit older? You can give it certain character traits, emotions, the unique features that will set your product apart from the rest.The questions below will help you to understand your ideal user better. If you are working on an internal chatbot, you can skip these questions. However, they might help you to understand the necessary features of your future chatbot.Take your time and think:– What is the average age of a person who will be communicating with your chatbot?– What kind of job does he/she have?– Is he/she working at the office and uses a laptop?– Or maybe he/she is doing the job in another way?– What are his/her interests?– What problems can occur in his/her everyday life so the chatbot can become the best solution for him/her?Step #3. Create a planThe next thing to do is to create a chatbot project plan and requirements. To quickly develop chatbot requirements, you can use our short brief that will help you with that. Here’s our brief template.If you need help with shaping your idea into a chatbot project plan or need a free consultation on chatbot development, please contact us.Before starting creating chatbot requirements, it’s worth to analyze your targeted user again and research his behavior, habits, preferences, goals, and motivation. This will help you to better understand what problems your users want a chatbot to solve.But don’t overthink this part. When developing requirements focus on things like bot use cases, bot user stories and bot business goals.Bot Use Cases.Think and describe all the necessary functionality of your chatbot. It’s like writing what bot will be able to do.For example:A bot will be able to answer the user about the dosage of aspirin.Bot User StoriesNow, describe how you see your chatbot from the user side by completing the sentence: “As a user, I can..".For example:As a user, I can ask him a question about the side effects of ibuprofen.And a small tip for you, in this section try to break it down into smaller chunks. Think about what will bring value to the user, what are his goals and needs. Try putting yourself in the user’s shoes to understand what a user wants to get from a chatbot and what he wants to achieve.Bot Business GoalsLike any other software product, chatbots take time and effort to develop. But chatbots are also driven by its business goal. Think of the most important business goals you would like to achieve with it.For example:“We want to achieve 50% pressure reduction on our customer support by the chatbot”.To make the requirements even more understandable, take advantage of the draw.io resource. You will be able to build a chatbot flow in the form of a decision tree that helps you to understand how the bot will interact in any case. This will allow your team and you to see how the chatbot should work.Step #4. Build a prototypeNow it’s time to take your idea and check if it works. This means that now you can start developing the chatbot prototype.There are two ways to develop a prototype without coding skills required from you.First, you can take advantage of chatbot building platforms. Secondly, you can hire a chatbot development team to consult and help you to create a prototype. Both options work great and which is the best for you depends on a chatbot that you want to build, your business needs and on resources like money and time.Chatbot builderTo develop a prototype yourself, you can use chatbot builders. Chatbot building platforms are a great option if you need a fast and cheap prototype. Usually, these platforms work in drag`n`drop mode where is no programming required which makes them easy to use for everyone. There are many different chatbot builders, but the most popular are Manychat, Chatfuel and flow XO.Here’s a list of the most popular chatbot building platforms: https://www.ventureharbour.com/best-chatbot-builders/When developing bots using builders, you can face some troubles due to limited possibilities of platforms. Most of these builders focus on marketing and have a small range of customization and functionality. Additionally, not all platforms provide support and maintenance for a chatbot. However, if you need a simple prototype with basic features, the chatbot builder will be a great fit for you.Chatbot development teamThe other way is to reach a chatbot company and assign all the hard work to them. So, instead of wasting hours in chatbot builders, you can contact professionals to develop your prototype. Hiring developers can be more expensive than using chatbot platforms, but this will save your time and enable you to add custom features to your prototype. Moreover, a chatbot company can build you a scalable prototype which means that afterward, you won’t need to rebuild the chatbot from scratch. So, you can keep upgrading, training and developing your already made prototype instead of starting building from the start again. With a chatbot development team behind your back, you’ll be able to quickly scale your prototype to MVP and then to a fully functional product.Be sure to contact us if you need a consultation or help with a prototype.Essential things in Healthcare chatbot development1. SecurityIn the healthcare industry, security of patients’ personal information is crucial. To ensure full protection of patient’s data, companies should use the highest security practices like data encryption, and “on-premise” deployment. Also, when developing a healthcare chatbot, it is important to pay attention to GDPR or HIPAA compliance.What is GDPR?The General Data Protection Regulation (GDPR) is a regulation on data protection for European Union citizens. It also applies to the transfer of personal data outside of the EU area.What is HIPAA?The Health Insurance Portability and Accountability Act (HIPAA), protects sensitive patient data. A company that deals with protected health information (PHI) must follow the required physical, network, and process security measures.GDPR vs. HIPAAThe most significant difference between these compliances is that GDPR covers citizens of the EU while HIPAA is restricted to American citizens and healthcare companies.GDPR compliance requires an active patient’s consent before storing any of their personal information in the database. On the other hand, HIPAA allows healthcare companies to freely process patient's data while it is being stored and transmitted with proper security standards. Unlike GDPR, HIPAA regulation doesn’t give a patient a right to erase their records from a hospital’s database anytime they want.How to become compliantIf you want to learn more about GDPR and HIPAA here are useful guides with checklists:How To Make Your Chatbot GDPR CompliantHIPAA Compliance Checklist2. Consider using MicrosoftIn recent years Microsoft started actively investing in Healthcare. The company has created everything you need when building chatbots. Microsoft has their server- AZURE, an NLP engine – LUIS, Microsoft Bot Framework, and a chatbot platform – Health Bot Service.To develop using Microsoft products, you have to code. So, you can ask Microsoft team to come and help you with that. This can be expensive, and you can search for a third-party vendor that will build your bot using Microsoft software. You can find top chatbot developers in this list from Clutch – B2B ratings and reviews platform: https://clutch.co/developers/artificial-intelligence/chatbots3. Natural Language Processing– Train a chatbot with available dataWhen building chatbots, it’s important for your bot to gain trust and credibility. The healthcare industry operates a specific terminology and to look like an expert, your chatbot has to speak like an expert. So, it’s essential for a Healthcare chatbot to have a vast database to know many variations of a medical term. To create a database you can use old data from your current customer support. Find previous interactions with your customers. To do that, you can review call logs and scripts, email chains, analyze FAQ pages. It’s also worth considering your personal or customer representatives' knowledge and experience. Think about what are the most repeating questions and issues your users stumble upon. Consider which of these questions, words, phrases your chatbot has to understand.– Train a chatbot using pre-made datasetsThe other option is to use pre-made ready-to-use datasets. These datasets are great when you need to train your chatbots NLP fast, or you don’t know where to start. They already have questions and answers and can help you cover the basic topics. Here’s a great list of datasets that you can use for bot training: https://gengo.ai/datasets/15-best-chatbot-datasets-for-machine-learning/Don’t forget to upgrade your chatbot vocabulary with phrases from the conversation with real people. Analyze how users interact with the bot and then adjust chatbot’s tone of voice or expand its vocabulary based on real feedback.4. Double-checkingIf you are building a medical chatbot, it’s important to mention that they aren’t supposed to diagnose patients. Instead, chatbots can collect medical information and transfer it to a doctor so the patient could be diagnosed faster and more efficient. Healthcare chatbots can interview patients, putting down symptoms and important medical notes.In the healthcare industry, it’s important to make sure that the patient’s medical information is accurate and correct. So, it’s appropriate for healthcare bots to ask many questions and double-check when needed.Interested in Healthcare chatbot development? Read more in our guide for Healthcare Enterprise Chatbot Development: 8 Proven Ways For Healthcare Enterprises To Automate Routine Work Using Chatbots - BotsCrew