1 Digital Health Form.

 

The health questionnaire (HQ) is one of the most critical and sensitive aspects of the underwriting process for personal insurance, such as health insurance. From a fully digital perspective, it requires complex analysis and decision-making that go far beyond simply converting the paper questionnaire into a web form—which is what most companies that have begun the journey toward digitizing their processes have done so far. We’ll try to focus on the most relevant points for implementing this process, keeping two key starting points in mind :

 

• The importance of the portfolio of services: The clinical analysis of a particular health questionnaire can result in a subsequent proposal of exclusions, which in the digital health insurance model takes the form of a portfolio of excluded services. This is an important point to which we will dedicate an article of our own in this series, where we will describe the portfolio of services of a health insurance coverage. We anticipate that this is a fundamental armor for the digital management of this line of business and is directly related to the prior assessment of risk.

 

• The adaptability of the questionnaire: It should also be said that, depending on the level of risk of the coverage, the questionnaire should be different. For example, applying for health insurance without hospitalization is not the same as incorporating this coverage. In digital processes, handling different forms or questionnaires is much easier and more flexible than doing it on paper. Moreover, the questionnaire can be unique, but build it inclusively with different questions – not according to the product requested, but according to the coverage. In other words, it is possible to go into much greater detail and, in this way, gain precision in the information obtained and in the selection of the risk to be taken. A company that handles this type of questionnaire must have insurance software that allows different models to be configured according to the coverage requested.

 

From a fully digital perspective, the health questionnaire requires complex analysis and decision-making that go far beyond simply converting the paper questionnaire into a web form.

 

 

 

2 Type of questionnaire for risk selection

 

Different approaches to the level of detail in a health questionnaire naturally have their advantages and disadvantages. We won’t analyze these details—which are generally determined by factors such as the distribution channel, the channel’s specialization, the marketing campaign, or the target audience, among others—but we will describe them in general terms.

It could be said that there are four possible orientations:

 

2.1. Detailed health questionnaire:

 

Of many questions about the client’s health status. If this is the approach, the company should ensure that the answers are multiple-choice and simple (for example, yes/no or check the appropriate box). Each question should include an information icon to explain in detail the meaning and interpretation of the question.

 

2.2. Short health questionnaire:

 

It can be reduced to three issues. Inquire into the clinical past (past pathologies), the present (current health status) and the future (if treatment is planned in the near future). . In the affirmative case of any of the questions, you can choose to collect more information through an extended questionnaire or through a semantic assistant that helps the user in the description of the pathologies to which it refers.

 

2.3. No health questionnaire:

 

It is certainly a more controversial alternative, but let’s make this reflection. The Insurance Contract Law protects the insurance company. It is well known that no event prior to the contracting of any insurance is covered by it. In the case of health insurance, the client cannot deliberately conceal from the company the diseases or pathologies from which he/she suffers. Strictly speaking, therefore, the health questionnaire should not be necessary as long as it is established that the insured is aware of this point.

Clearly communicating this fact to the insured—that their preexisting conditions will not be covered—and having them agree, even through a sworn statement, that they acknowledge and are aware that preexisting conditions and treatments are not covered, could be a solution that would avoid this process and its consequences. Alternatively, the client could simply be asked whether they believe it is necessary to disclose any significant medical conditions related to the services to be purchased, emphasizing that no preexisting conditions will be covered.

To reassure technical experts in the field, we can say that in the new digital age, we will have new tools that allow us to detect pre-existing conditions much earlier. This is one of the advantages of having immediate access to information about what is happening in insurance management software regarding the benefits system—a topic that will be covered in another post. Nor should we forget that waiting periods for services are in place to minimize adverse selection.

This model not only protects the company, but also the insured, who sometimes takes out insurance by answering the questionnaire ambiguously or incorrectly – often just out of clumsiness. Subsequently, this can lead the insured to enter into a confrontation with the company for not covering an ailment when, perhaps, he has been paying the premium for some time. The bad reputation of the sector stems from events such as this, and it is therefore very important to reflect on how to improve the selection of risks prior to taking out insurance.

 

 

 

 

2.4. Health questionnaire conducted by a physician or AI assistant.

 

The perfect scenario is to have a medical professional conduct the client questionnaire. In this way, he/she will be able to understand the scope of the questions and the physician will be able to inquire into the relevant issues based on the information provided. Why don’t companies use this type of questionnaire if it is the most appropriate? This is done in some types of insurance, for example, for life insurance with high capital sums. However, it is not common practice in health insurance contracting because of the high cost and, above all, because of the complex operational process that must be implemented, with the management of appointments and face-to-face medical evaluations.

However, through AI models of natural language processing it is possible to reproduce this questionnaire without incurring additional costs. For them, an intelligent conversational virtual doctorchatbot could be used, where the system processes and deepens the questions asked to the customer according to the answer he/she gives.

 

Through AI models of natural language processing it is possible to reproduce the questionnaire performed by a medical professional.

 

 

 

 

3 Individual or group questionnaire.

 

Let’s imagine a customer signing up for health insurance on the company’s website. You are interested in a family policy for several members of your family. When filling out the questionnaire, each of them should complete or at least sign their own. Without going into detail on the issue of the minors and the confidentiality of the process, it does not seem very pragmatic for the whole family to get together to carry out this procedure. A moment of friction in the pursuit of the sale that is worth analyzing. There are two possible solutions to this situation:

 

• Email: One solution is for the subscriber/insurance policyholder to provide an email address for each of the members who are required to complete the health questionnaire. The process will send to each of them, a link to that email where they will be presented with the health questionnaire that they must answer and sign. The subscription will be stopped until all completed questionnaires are submitted to the company. It would also force the company to design workflows with process milestones: customer reminders, receipt of questionnaire, pending assessment, etc.

 

• Signature of validation: A less rigorous but slightly more pragmatic approach is for the policyholder to fill out the questionnaire on behalf of everyone. In this case, each member would then have to verify, confirm, and sign their own questionnaire. At what point would this take place? When the policyholder logs into the Private Area provided by the company. In fact, this would be the first step in the insurance company’s Welcome Experience and would be essential for the policyholder to activate their Private Area.

 

An important aspect of insurance underwriting—in a truly digital model supported by suitable insurance software—is that the process of signing health questionnaires can proceed in parallel with the policy issuance process until the policy is underwritten. What does this mean? The company could issue the policy but not activate coverage until certain conditions are met or signed in the policyholder’s Private Area during the insurance onboarding process—a key moment in the customer experience. Thus, in the case of the health questionnaire we’re discussing, the company can activate coverage on an individual basis as each family member accesses their private area and signs the questionnaire that has been presented.

With this formula, the company will decide the partial/total issue conditions and manage them more precisely.

 

An important point in the insurance underwriting, in a truly digital model supported by a suitable insurance software, is that the management of the signature of the health questionnaires can go in parallel to the process of issuance until the underwriting of the policy.

 

 

 

 

 

4 Semantic Assistant with AI and coding techniques

 

The major innovation in the digital health questionnaire underwriting process will come from the use of assistants and semantic analysis with Machine Learning techniques. As we have already mentioned, the ideal questionnaire model is one that is assisted by a virtual doctor using an intelligent chatbot with natural language processing (NLP) technology, i.e. a system that understands and processes human conversation, applied to a medical conversation between doctor and patient.

Starting from the design of a first medical questionnaire experience, the machine learning algorithm will allow the system to learn and improve. If at the beginning your assessments will require the final review of a medical professional, the tool will gradually become more reliable until it becomes autonomous.

When asking the user for information about their medical conditions, the user will describe them in their own words, which may not always be in technical terms suitable for assessment and underwriting. The semantic assistant, based on natural language processing (NLP), will interpret what the customer says, suggesting concepts and descriptions based on a standardized table of medical conditions and treatments— for example, ICD-9/10/11. This would allow us to achieve several objectives:

• Assist the client in completing their health questionnaire with accurate and appropriate terms.
• To have a questionnaire with rigorous, detailed and standardized data.
• Most importantly, the result will be a CODED HEALTH QUESTIONNAIRE. This will make it easier to manage risk assessment and apply underwriting rules, either automatically or supervised. We will analyze how this aspect is linked to the excluded services portfolio.

This semantic analyzer can even be applied to a medical report to automatically extract and code the clinical references it contains about a patient.

Obviously, on coded pathologies and treatments it is easy to apply automatic rules that reflect the risk selection criteria that the company wants to apply. In addition, this minimizes the subjective judgment that could occur when a company’s risks are analyzed by different underwriters.

 

The algorithm developed by artificial intelligence and machine learning will allow the system to learn and improve itself. If at the beginning your assessments will require the final review of a medical professional, the tool will gradually become more reliable until it becomes autonomous.

 

In conclusion, regardless of which questionnaire a company decides to use, it should rely on a virtual assistant that replicates a doctor’s assessment and questions and overlay this questionnaire with a layer of semantic analysis to understand, code, and maintain the responses and information collected within it in a coded format. It is a priority that current health insurance management software incorporate Artificial Intelligence into: the incremental design of questionnaire questions based on the requested coverage, the dynamic configuration of the questionnaire based on the responses provided, and finally, the analysis and structured coding of the responses. The resulting increase in accuracy will benefit not only the company but also the customer being insured.