Legal Help Online Dashboard

Nóra Al Haider and Margaret Hagan

Digital Legal Needs analysis of an online legal clinic to predict seasonal trends in people’s legal needs

Stanford Legal Design Lab collaborated with the American Bar Association to analyze ABA Free Legal Answers. Free Legal Answers is an online legal clinic through which low-income individuals get answers to civil legal questions from lawyers, completely free of charge.

The Lab has analyzed people’s questions from the clinic’s data from between 2012 to 2019. During this period, there were tens of thousands of questions asked across the many states that Free Legal Answers is offered. Each of the questions was self-labeled by the user (or, by the platform administrator) with the broad legal category it belonged to — like Family, Housing, Veterans, Employment, or Consumer.

Digital legal needs analysis of the clinic’s questions has helped us identify what trends exist in people’s use of the Free Legal Answers clinic and what needs they are coming to the clinic to get help with.

What can we do with this digital legal needs analysis? It helps the ABA and other legal service providers to develop smarter tools and strategies to address clients’ needs.

Our first focus has been on seasonal trends throughout a calendar year:

• When should legal services hold public education campaigns about legal needs?
• When should they conduct marketing and buy ads?
• When should they be recruiting volunteers to serve more people?
• After a natural disaster, when are people seeking help?

Our second focus has been on getting the messaging right. What words should providers use in outreach and advertisements, to resonate with the target audience?

• What phrases do people use to describe their legal needs?

This report has recommendations for the ABA and other legal services groups about how to use data to best communicate legal information, mitigate the effects of legal problems, and recruit attorneys to assist. Digital legal needs analysis has the potential to predict legal problems before they occur, thereby enabling advocates to pre-empt access-to-justice challenges at the outset.

1: General Outreach for Legal Services in the Late Summer to Early Fall ​

Generally, the heaviest usage of Free Legal Answers occurs from August to October. This holds true across various states, for several years, and across most legal issue areas.

During these months, if there was wider outreach (through marketing campaigns, events, and other channels), then a wider group of people — who might also be experiencing a spike in legal needs during these months, but who aren’t aware of legal help services — may become aware of Free Legal Answers.

2: Seasonal Issue-Area Targeted Outreach​

During known seasonal spikes for particular issue areas, there might be outreach targeting these specific needs.

For example,

• Questions related to education problems peaked in February-March and August.
• Income maintenance questions peaked between February and April.
• Work and employment questions peaked during October to November.
• Sexual assault questions peaked in July and October (and dropped significantly in February and March).

These seasonal peaks can guide marketing and event outreach, in which the legal services community coordinates issue-area campaigns to engage a wider group of people who potentially have these needs during these times. It might be through special awareness months, series of clinics and know-your-rights events, advertisement purchases, news media collaborations, or events with community partners.

3: Preventative Public Education During Months Before Spikes

Data-driven action should not only be taken during the month in which the trend or decline of a legal issue takes place. Data analysis can also be used to take preventative action.

For example,​ ​if family issues are spiking in late summer and early autumn, then there should be proactive public education campaigns in the preceding months that give preventative information about family law problems. These preventative resources should come at the key time when people are beginning to have questions or issues, but they have not yet escalated.

4: Seasonal and Issue Specific — Volunteer Recruitment

Data provides an insight into the peak times on the platform. This might mean that during some months more volunteer lawyers and students are needed.

Based on the data analysis, predictions can be made on when to start recruiting and training volunteers to deal with the high volume of requests. Recruitment decisions can also be made when data indicates that certain issues are high in demand.

For example, income maintenance questions peak between February and April. During these months volunteers with this issue area specialization should be recruited.

5: Post-Disaster Legal Help Sequencing

Legal service groups can be prepared to serve the particular sequence of legal needs that emerge after a flood, hurricane, wildfire, earthquake, mass shooting, pandemic, or another disaster.

This means distributing particular resources and ensuring there is service capacity for issue areas in the immediate weeks after the disaster hits, and then in the long-tail of months and years afterwards.

6: User Keyword-based Outreach​

When legal service organizations are doing outreach to engage a wider public in preventative education or services, they can make use of keywords that people use when talking about particular legal problems.

This approach can help inform how outreach is phrased, what adwords are bought, and how materials are presented. Rather than communicating in legal categories (like housing law, landlord-tenant issues, or unlawful detainers), the outreach can instead reflect the most common phrases that people use for an issue.

For example, our Reddit keyword modeling research, drawn from posts on r/legaladvice, illustrated the following common phrases that people use:

Housing legal needs phrases​

There were several housing categories in our Reddit keyword modeling research. One category focused on tenant-landlord relationships.

The most commonly used phrases for this category were: security deposit, deductions, return security, 21 days, 45 days, withheld, wear tear, normal wear, written notice, itemized, certified letter, forwarding address, notice given, days prior, carpet cleaning, cleaning fee, walkthrough, tenant shell, lessor, court, small claims, manager, management, landlord and tenant.

Work and employment legal needs phrases​

There were several employment categories in our Reddit keyword modeling research. One of these categories focused on employment contracts.

The most commonly used phrases for this category were: contract, employment, signed, shall, offer, sign, employer, current employer, job offer, clause, termination, severance, non compete, notice, unemployment benefits, offer letter, week notice, resignation, written notice, new contract, enforceable, bonus and commission.

Family legal needs phrases

There were several employment categories in our Reddit keyword modeling research. One of these categories is focused on assault, violence and abuse in the home. The most commonly used phrases for this category were: police, charges, called, help, family, friends, sister, brother, home, tell, told, neighbor, mom, mother, scared, happened, threatened, sexual, sex, sexual, violence, kill, assault, physically, rape, screaming, yelling, pictures, media, door, room, cat, dogs, inside, bathroom, animal, gun, ill, suicide, rape, stalking, weed, meds, drunk, eye, and face.

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Our Lab is continuing to work on using the data to improve the services & experiences for people seeking legal help online. The ABA Free Legal Answers team, Baylor Law School, and our Lab have been leading a project to identify and answer FAQs in the highest volume legal need areas.

These FAQs can be given to people after they ask a question, and are awaiting a response from an attorney. The data helps us spot the most common questions, and also phrase them in a way that makes sense to a person who is not a lawyer.

For more information about ABA Free Legal Answers, see Ambar.org/fla or contact Tali Albukerk at tali.albukerk [at] americanbar.org.

In our recent Legal Help Online Cohort meeting, we asked the question: what are legal terms that people often get confused? Where they say one term-of-art, but actually mean another situation?

These high-confusion terms are important. People could rely on the wrong information if they are visiting web pages or following guides for the wrong term.

Here are the legal terms that people get confused by:

1. guardianship vs conservatorship
2. joint custody vs legal custody
3. parenting time vs visitation
4. expunction vs expungement vs record-sealing vs record-masking vs clearing a record
5. separation vs divorce
6. restraining orders vs protective orders
7. custody vs legal decision-making vs parenting time

Do you know other terms that confuse people? We’d love to hear them — so we can devise strategies that help people learn what their situation is actually called.

How can more legal help providers get more of their information & guidance into more languages?

There is a huge language access problem in legal services. So many people who need help have issues with Limited English Proficiency. It would be better to get these LEP users to articles, guides, FAQs, and services in their own native languages. But there is not enough funding, staffing, and capacity to provide robust information & services in all languages needed.

Especially since each jurisdiction or organization is having to do language access on their own — it becomes a huge budget & capacity issue.

A 2013 report for the Legal Services Corporation, “Can Translation Software Help Legal Services Agencies Deliver Legal
Information More Effectively in Foreign Languages and Plain
English?“ by Jeff Hogue & Anna Hineline (pdf at link), outlines different strategies that legal aid groups can use to increase the capacity & accuracy of language access efforts.

They outline various tech strategies that could increase this capacity to serve in multiple languages:

1. Machine Translation (like a variation of Google Translate), in which a computer program is receiving the text, and proposing the translation
2. Human Translation, in which a person is proposing the translation based on their knowledge of language & the situation
3. Translation Memory, in which people record their translations into a database, and then when there is a new text to be translated — they draw on this existing database for the translation

This third category — of a shared database of translations and glossaries — could be a powerful solution to get to scaled, accurate language access. What if legal aid groups & legal help websites shared their multi-lingual (and plain language) translations of paragraphs, sentences, phrases, and words?

If there was a collective, open-source effort to create a Translation Memory database, this could spread the costs out among many groups. Instead of each group translating their content, they could share their past translations and allow other groups to draw from this.

This can also avoid the potential harms of a machine translation solution. In that setup, the providers are hoping that the machine (and its algorithms) can provide accurate & understandable translations. They might have a human to help review this. But the Translation Memory approach prioritizes the expert human translation from the start, and then uses technology to make that approved, hand-crafted translation more accessible and replicable.

The authors of the report highlight that this shared Translation Memory approach could be valuable but costly. Here are some of their recommendations:

• “The amount of time and effort that needs to be put into developing and maintaining a high-quality glossary and translation memory is non-trivial. We recommend that the Legal Services Corporation convene a group of leaders from legal services providers, plain language experts, and court leaders to adopt or discard this approach.” (page 23)

They also recommend gathering a similar group of stakeholders to explore what is ethically & technically possible with combining machine translation with human review or specialized legal glossaries. Could there be an effective way to build on top of Google Translate or Microsoft Translate? It would be important to have a group of stakeholders and expert reviewers decide if this is possible and ethical.

For either a Translation Memory or Machine Translate + Human Review approach, having a shared database of glossaries is a key step. Our team at Legal Design Lab has started gathering glossaries that already exist, to start building an open-source database of legal help-oriented translations.

Please feel free to write or share if you want to work on this project with us! We hope to push language access forward with this infrastructure work, that can lay the groundwork for more accessible and scalable legal help efforts.

Nora al-Haider, Luz Daniel, Shobha Dasari, Margaret Hagan, Arianne Marcellin-Little, Alistair Murray, Michael Perlmutter, Roland Vogl, and Annie Zhu,

How could computable contracts improve people’s health insurance contracting?

In Winter 2022, our team at the Legal Design Lab worked with our Stanford Law Colleagues at CodeX to teach the class “Human-Centered Computable Contracts”.

This is part of ongoing work at both the Legal Design Lab and CodeX. Our Lab has been working on improving contracts, terms of service, and other legal text that people must grapple with to protect themselves. We’ve taught classes at the law school and d.school on these topics & have documentation of what we’ve been learning.

CodeX has made computable contracts a central theme for the coming years. Their Insurance Initiative is pioneering new ways to make contracts machine-readable, create a standard language for contracts, and pilot new ways to improve contracts in insurance use cases.

Our goal with the course — and ongoing design work with computable contracts — is to make sure that as this new technology develops, it’s done with real people’s concerns, frustrations, capacity, and dignity at the center.

Of all the ways we can improve the infrastructure of contracts and how they are deployed, what will people be able to use — and to get them better insurance and health care?

Key Opportunities for Human-Centered Computable Contracts

Before diving into the details of the class, our user interviews, and our initial brainstorms, it’s worth jumping to some of the big takeaways. What should people working on improving contract experiences be focused on, to truly solve people’s fundamental problems?

“Let Me Know What You Know”: Tools to Address Information Asymmetry

The most central problem in the consumer-insurance provider-health provider relationship is information asymmetry. Even if you are a power-user, who is doing everything you can to figure out how to be wise when it comes to saving money and getting the necessary care — you still cannot find out what things actually cost until after the event has happened, choices about care have been taken, and claims have been filed.

People, especially more proactive users, want tools that start to balance out this knowledge. They want tools to help them

• know before purchasing a policy how it will play out in key situations they expect might happen (a back surgery, an urgent care visit, a pregnancy, disability support, etc…)
• know what different claim codes will be covered or not, before they actually go for service from a certain medical provider and with certain claims being raised

This could be in the form of chatbots, price predictors, shopping quizzes, or even more intelligent phone calls with customer service. But they want to know what the customer service reps at the insurance companies and health care providers know. What are the real prices of things? What are the possible ways a messy life problem might be encoded into claim numbers? And what are the strategic decisions a person can make before they get encoded into a certain claim path & have to deal with the bills that might follow.

Computable contracts, paired with open data sources from health providers or insurance companies, can be a foundation for these tools to address information asymmetries.

“Give Me Something I Can Rely On”: Tools that Give Ground Truth & With Assurances

Information tools are not enough. Many consumers have been burned by previous interactions with insurance or health providers, where they have been given information by a customer service rep or policy — and then found out later that it was not reliable. A bill ended up being way more. A procedure wasn’t actually covered. The provider wasn’t actually in the covered network.

More proactive consumers try to get to a ‘ground truth’ right now by triangulating extensive research. They call the insurance company’s customer service reps multiple times, to speak to multiple people, and compare their responses to find out what they can rely on. They go to Reddit boards, Facebook groups, and chat with friends to find other people who may have ‘ground truth’ experiences that are comparable. What will actually happen to me? Who can I trust to tell me the truth?

Recent policy changes in the US may let consumers contest bills that turn out to be surprisingly high. Another direction would be for providers to have to honor the price that a person receives from a computable contract-powered tool.

A person, before they use a service or buy a policy, can use an intelligent tool to get a prediction of the out-of-pocket cost and claim coverage for a certain service. They can save this and rely upon it. If they do use this service, and the price turns out to be higher or the claim is denied, then they can show the prediction to contest the decision and protect themselves.

Computable contracts tools’ value will be in how reliable and binding they are. They need to give some guarantees to the consumer to get to the fundamental mistrust and betrayal that most consumers have toward their insurance companies.

“Why Does This Have to Be So Crummy”: Design a Claims Process that is Empathetic and Supportive

When people try to make use of their insurance policies, often the process is murky, painful, and stressful. The consumer is often the last to know what is happening, as the other 2 parties — the insurance provider and health provider — are in communication making important decisions.

Plus there are opaque and overwhelming statements that come to the consumer, about possible amounts they may owe, claim codes about what services they have used, and ultimately an amount due as soon as possible, or a collections company might start hounding them.

The user does not feel like she is in control, or has a sense of dignity. There is no delight in the claims-making or -processing journey. People feel like no one is on their side — and the other 2 parties seem to be ganging up on them, to try to push their companies’ financial responsibilities onto the person with the least power and money. People want an advocate, someone on their side.

Computable contracts, mixed with new service design-oriented offerings, could help transform this process. What if there was more transparency & sense of control for the user before, during, and after the claims process? What if they felt that the price they were paying was agreeable, worthwhile, and acceptable — because they had more of a choice in deciding to make use of the service at this price, and because they have tools to contest it when it is too high.

Even more, insurance providers could think about proactively giving service maps — -with expected claims, services, and costs, to people who are on a certain medical journey. Whether a person is starting off with a pregnancy, fighting a disease, or treating a disability, the data about past claims and costs could be used to provide sample maps to consumers about what other people have done to make use of medical services in wise, financially affordable ways. The insurance provider can use its knowledge of so many consumers’ journeys to help a person plan out their use of services and risks they will take, to make sure they are doing it as wisely as possible.

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The Class’s Basics

Our joint class Human-Centered Computable Contracts was taught as a policy lab, meaning we were able to do project-based work in partnership with a public interest group. In this case, our partner was the federal government group CCIIO (pronounced suh-sigh-oh), the Center for Consumer Information & Insurance Oversight. It is part of the US Centers for Medicare and Medicaid Services.

During the 9-week class, we had two main phases: exploratory interviews with people about their health care insurance contract experiences, and then prototyping and testing possible interventions (including around computable contracts) with people. Our goal was to learn more about whether and how computable contracts could benefit people in their health care and insurance activities.

We taught the students many service design techniques to make sense of the interviews and research: journey mapping, persona creation, and user story-telling. In addition, we did creative brainstorming through different structured activities. We drew from formal presentations on what computable contracts are — to then think through: how exactly could we make them useful to the people we’ve spoken with? The students made concept posters and tested the top five concepts with users to get their feedback.

We had a terrific, tight group of students who came from a mix of backgrounds. We had law students, computer scientists, and public policy students. Some had past experience as practicing lawyers, health care policy analysts, and technologists.

User Research to understand people’s journeys through health insurance

We began the class with the simple question: What are people’s experiences with health insurance contracts? And we also held on to a second question (more for the second half of our class): What are key opportunities for Computable Contracts to improve experiences & outcomes in health insurance?

We took a design approach to answer these questions. That meant talking to many stakeholders, including people who have been consumers and users of health insurance, as well as experts. During the quarter, the students and teaching team conducted 10 user experience interviews and 6 user testing interviews.

The teaching team recruited interviewees through social media advertisements and an intake screener. They signed up from around the country, and with different economic and educational backgrounds. Each interviewee had some experience with health insurance — some had been through multiple plans, others had their first insurance purchase this year. Each interviewee was interviewed over Zoom for between 20 and 40 minutes and compensated with a $40 gift certificate.

In the user experience interviews, we asked insurees to discuss their best and worst experiences with health insurance. We particularly asked users to discuss their experiences shopping for health insurance, making claims, and understanding coverage or prices. In the user testing interviews, we asked users to share their opinions regarding five different ideas about ways to help someone with their health insurance.

Insight into user experience was also shared in presentations and feedback sessions by Rogelyn McLean (Senior Advisor at the Center for Consumer Information & Insurance Oversight), Gary Cohen (former Vice President of Government Affairs at Blue Shield of California), Clara Bove (Researcher at AXA), Raphael Ancellin (Lead Product Manager at AXA), Pierre-Loic Doulcet (Computational Contract Engineer at AXA), and Michael Genesereth (Research Director of CodeX).

In addition, the team looked at past user research into people’s experiences with health care and insurance. The Enroll UX 2014 efforts, around the rollout of the Affordable Care Act, has very useful documentation of their user research into health care insurance customers.

What We Found in User Interviews

From our interviews, we learned that one size does not fit all when it comes to user needs and preferences.

Insurees’ needs and behaviors are influenced by their level of health insurance literacy and proactivity in seeking to fully understand their plan. However, regardless of specific needs or circumstances, all users want to save time and money in the processes of choosing, understanding, and using a health insurance plan.

Currently, information asymmetry between insurance companies and insurees is a source of time and cost inefficiency for the latter, who may be hindered in choosing the best plan or medical care to meet their needs if relevant information about insurance plans is inaccessible (or accessible only through a time-consuming search) or difficult to compare.

In addition, more information does not lead to more empowerment. Often the information available is obscure or unreliable. People feel like they can’t get a consistent, straight answer from their health or insurance providers about what will be covered and how much they will have to pay. There is also choice overload, with the process asking a consumer to make too many complex choices to be strategic. At some point, many consumers just give in and accept what is being told to them by the more powerful other two groups (the insurance and the health providers). They feel like they cannot navigate the process to protect themselves.

Many insurees do not trust insurance companies to provide full and accurate information or to act in the insurees’ best interests. Information asymmetry is a driving factor in this mistrust. While health insurance literacy is, for some users, a barrier to choosing and getting the most of out of a health insurance plan, even for very literate users, understanding their plan is challenging when information is unavailable, difficult to locate, or out-of-date. For instance, consumers desire more information about in-network healthcare providers, particularly regarding the cost and quality of care.

Pain Points

Choosing a Plan stage

The key pain points at this stage are time inefficiency, choice overload, health insurance illiteracy, and difficulty accessing or finding information. According to our user experience interviews, insuree frustration and time inefficiency may result from unfamiliarity with health insurance terminology, as well as difficulty finding and comparing information about different plans’ costs, coverage, and healthcare provider network.

Understanding a Plan stage

The key pain points at this stage are time inefficiency, receiving conflicting answers or vague responses from insurance company representatives, and difficulty accessing or finding information. For example, one user noted, “It’s really frustrating when you talk to different reps and get different answers. I call twice with any question to make sure their answers are the same. If they’re not, I call a third time. It’s crazy to me that someone like me who works within the system still has trouble with it. Insurance is too businesslike and not really trying to help patients. We need advocates within the system!”

Using a Plan stage

The key pain points at this stage are time inefficiency, surprise costs, inaccessible or unresponsive insurance representatives, and information asymmetry (especially regarding costs and the healthcare provider network). Having to choose whether, where, or how to receive treatment without cost information is an oft-cited insuree pain point. Ascertaining the role of referrals in insurance coverage can also cause uncertainty and stress.

Personas of health insurance users

In our user interviews, we learned from proactive and reactive users about their experiences at the stages of choosing a health insurance plan and filing claims.

At the shopping stage, proactive users may be focused on developing their literacy and fully understanding the plans that they are considering. This can be a time-consuming process, especially due to an overwhelming amount of information or plan options. Proactive users are often shopping based on specific needs, such as geographic scope or coverage of particular health conditions.

Reactive users, on the other hand, may have broader comparison concerns, such as finding the cheapest plan or the broadest healthcare provider network. Reactive users may primarily view insurance as a source of “peace of mind.” They therefore may be less motivated to examine all the details of their plan, and may rely on overall ratings or colleagues’ impressions in their decision. For instance, one user who had not yet needed to use his new insurance plan stated, “I will understand it better when I have a real situation.”

At the claims stage, proactive users seek to understand their plan before taking action. They find it difficult and time-consuming to get information about coverage and cost of care from either their insurance company or healthcare provider. Proactive users may even forgo care if costs are uncertain. Some do not trust their insurance company to provide full and accurate information, so proactive users often turn to online sources such as Reddit or Facebook to ask questions, whether due to greater trust or convenience.

For reactive users, especially if they have waited until a medical emergency to look into the specifics of their coverage, the cost of care may come as an unpleasant surprise. However, it is important to note that information asymmetry makes the cost of care obscure for both reactive and proactive users.

People’s Stories & Quotes about health insurance

Whether a proactive or reactive user, nearly every consumer we spoke with sought advice or help from somewhere other than their insurance policy contract or insurance representative when approaching a pain point.

What will it cost me to take my kid to the ER?

One consumer, who described herself as “relatively well-informed,” found the resources provided by her insurer either unhelpful or incomprehensible. When deciding whether to visit urgent care or the ER when her child became sick, she first combed through the “fine print and terms and conditions” of her insurance contract.

When this exercise proved fruitless, she called her insurer and spoke directly to a representative. Unfortunately, she didn’t feel like her questions were answered and she was no clearer on whether it would be more affordable to visit urgent care rather than the ER. Before resorting to guessing, she visited a neighborhood mom’s Facebook group where she posed the question to the community and asked for their advice. She gained valuable information that was immediately intelligible and that she trusted. Whether the information she received was correct is hard to say, but it allowed her to feel confident in making a decision — something she didn’t feel after combing her contract for information or speaking with her insurance representative.

What should I do about my back?

Another consumer we spoke with found herself in a somewhat similar situation but wound up with a different result. After physical therapy failed to cure her back pain, she decided to undergo surgery, which her doctor assured her would resolve her injury. Her doctor promised to send a pre-authorization form to her insurer. One week before her scheduled surgery, she discovered the doctor’s office had failed to submit the pre-authorization paperwork.

When they finally did, however, the insurance agency told her it was impossible for them to approve the surgery so quickly. She argued for an expedited turnaround, which the insurer agreed to. But, on her way to the surgery, they told her they still hadn’t made a decision. She decided to forgo the surgery and continue enduring the physical pain instead of going through with the surgery without knowing how much of its cost she would be required to cover.

Here, the lack of transparency and the slow process of a seemingly discretionary authorization prevented a patient from seeking medical care she could have used. No Facebook group could have answered this question for her and given her enough confidence that the surgery would be covered to feel comfortable undergoing the surgery.

Who can tell me the real information about this insurance?

A third consumer spoke about different avenues of information gathering he sought beyond his insurer. Whether shopping for insurance or, like the two consumers described above, making decisions about medical care, he found it useful to peruse Google, Facebook, and Reddit and to speak with friends and colleagues. Like the other consumers we spoke with, he found insurers the least informative and most difficult to get a comprehensible answer from. A different consumer told us that she calls her insurer twice whenever she has a question to make sure the first answer she received was correct. If she receives two different answers, she’ll call a third time.

Many of the stories we heard were disheartening and frustrating. The stories described above are only a small sample, but they are representative of the sort of stories we heard during our interviews. In the end, everyone is operating under their own unique circumstances. It boggles the mind that consumers should feel they’ll get better information from a stranger on the internet who knows nothing about the idiosyncrasies of their needs or the contract they have with their insurer than they would by simply calling their insurance representative or reading through their contract of SBC. But, as it is, consumers are operating under a severe information asymmetry with respect to their insurers.

This information asymmetry is not resolved by insurance representatives, insurance contracts, or SBCs. As such, it causes consumers to look elsewhere for help. But the help they receive may not lead them to the best answer. Consumers are inefficiently spending their time gathering information and making cost-inefficient decisions about their healthcare that may have detrimental effects on their own health. Nearly every consumer we spoke with expressed despondency at the fact that nobody was advocating for them from within the system, that they were constantly on their own and information was constantly out of reach.

Agenda for Change Based on Users’ Experiences

The success of insurance marketplaces is related in part to consumers’ ability to understand health insurance contracts and make informed decisions[1]. Competition at the consumer level is likely to reduce prices and improve quality when a sufficient number of consumers make informed decisions[2]. However, consumers can also make suboptimal decisions when faced with overly complex choices[3] or too many alternatives[4].

Moreover, the information asymmetry permeating the health sector represents an obstacle to regulating and promoting competition within this market. In this sense, insurance purchasers who cannot understand health plan offers will find it difficult to make rational decisions regarding the insurance company they wish to contract.

Regarding the problems detected in the private health insurance market, the government has made progress in reforms to reduce information asymmetry and empower consumers to make better decisions. Among these reforms, we can highlight the following:

Hospital Price Transparency

This regulation requires the hospitals operating in the US to provide clear, accessible pricing information online about the items and services they offer. This information but be machine-readable with all items and services and must be displayed in a consumer-friendly format. The main objective is to make it easier for consumers to shop and compare prices across hospitals and estimate the cost of care before going to the hospital.

The government’s major reform efforts have contributed to improving this market. However, future efforts should aim to resolve the pain points consumers face[5].

Summary of Benefits & Coverage

This reform aims to present the consumer a snapshot of the health plan’s costs, benefits, covered health care services, and other essential features. The main objective of this regulation is to help consumers –in the shopping phase — to compare different elements of health benefits and coverage[6].

Metal plans

The Affordable Care Act standardized small-group and individual health insurance policies by creating a “metal” ranking. All the health plans are categorized into Bronze, Silver, Gold, and Platinum metal tiers. Each category offers different ratios of what you will pay and what your health plan will pay for your care.

The government’s significant reform efforts have contributed to improving this market. However, future efforts should aim to resolve the pain points consumers face, such as time inefficiency, choice overload, and lack of health insurance literacy. We believe that technology and computable contracts can be great tools to resolve these pain points we saw in the interviews. The adoption of computable contracting by the insurance companies will create improvements in efficiency for these firms and benefits for consumers[7].

Can Computable Contracts help?

In the second half of our course, we moved from general empathy and exploratory interviews with consumers — to diving into possible solutions.

Computable contracts, in particular, were discussed as a way to improve information transparency, speed of processing, and consumers’ ability to make strategic choices. The students heard from experts at CodeX who are establishing standards and pilots of computable contracts to hear how they might work in the health insurance space. Then they had to develop proposals about human-centered computable contracts to improve people’s health insurance experiences.

In insurance, the product is the contract, different from many other industries. From a consumer perspective, these contracts are often difficult to understand and this information asymmetry between insurers and insurees produces mistrust from customers in insurance companies.

In our research, we identified 4 main consumer pain points with regards to health insurance, which was the focus of our research:

1. time inefficiency,
2. choice overload,
3. information overload, and
4. lack of health insurance literacy.

What’s a computable contract exactly?

The automation of contracts through computable contracts presents a positive opportunity for both insurance companies and consumers. According to Stanford CodeX, “a computable contract expresses the rights, duties, and processes defined in a contract directly in machine-executable code for querying, analyzing, verifying, and automating contractual obligations.”

Legal rules have a well-defined logical structure that makes them feasible to define in a program. In a simple computable contract, we can program the definitions of events specified in the contract using a set of if-else rules that specify different circumstances, along with the consequences when those events occur.

By putting the understanding of a contract into code, we unlock many new value propositions in using computable contracts. From a consumer perspective, there is improved transparency and understandability of the contract. Through a query of the computable contract, a consumer can understand how their contract applies in different scenarios, such as getting the cost of a procedure that they need to be covered by their insurance company. The computable contract may also have FAQ functions or information visualization that will make it easier for a consumer to understand the terms of their insurance contract. Insurance representatives that interact with the contract, such as sales agents or customer service will also have an easier time answering consumer questions as a result.

Computable contracts also offer the ability for insurance companies and consumers to increase the customization of their contract and policy with the company. For example, the computable contract can be modular, meaning that a consumer can take elements from different policies that fit their needs the most effectively, and create a customized policy as a result, with little change to the typical operations of the insurance company.

Insurance companies also benefit from computable contracts due to increased efficiency in their operations. Claims processing under computable contracts might only involve a few queries to the computable contract, which will make this faster and lower cost for the company. Insurance companies may also benefit from simplified underwriting due to the automation and improved precision of actuarial calculations. Regulators also benefit from the use of computable contracts, since the more structured nature of computable contracts will support internal oversight and external regulation of insurance companies.

Additionally, computable contracts can unlock new opportunities for innovation in the insurance industry. Through more structured insurance data collection and analysis, insurance companies are able to create more opportunities for the improved effectiveness of data analysis and artificial intelligence tools and research. Insurance companies will also be able to use more thorough analytics about consumer preferences and improved actuarial models to improve their policy design and pricing. Increased interoperability between computable contracts can also help improve reinsurance transactions as well as improve quantifications of risks in pooling and shared risk schemes.

Prototypes for computable contracts in health insurance

This section details our prototypes of insurance products that utilize computable contract technology introduced in the previous section and the responses we received from user testing on these prototypes. These findings are used to inform general takeaways for insurance companies and government agencies for potential next steps.

Based on our understanding of consumer pain points with insurance, our research group created five ideas for prototypes to test, grouped together based on the stages of the user journey: 1) understanding insurance, 2) shopping for a plan, and 3) using the plan.

Prototype: Health Insurance 101

First, for the “Understanding Insurance” stage, we developed a prototype of an educational online training sequence which we call Health Insurance 101. The purpose of this feature is to address the lack of insurance literacy, one of the major pain points for consumers today. Consumers would be able to use this application to complete a series of short videos and quizzes to learn the details of the insurance policy.

Companies could tailor the program to educate the user about specific policies and they could require every policyholder to complete these videos and quizzes at the moment they purchase their coverage and every five years thereafter. Not only would these educational programs provide consumers with valuable basic information about their coverage and how health insurance works more broadly, but it also creates a base level of trust as transparency about policy is given from the start.

Computable contract technology would be valuable in the process of developing these programs as the first step of creating a computable contract is to identify and define domain ontologies, which can be translated into key learning points in Health Insurance 101.

Prototype: Healthcare Map

Next, for the “Shopping for a Plan” stage, we created a prototype of a Healthcare Map that allows people to view healthcare providers near a chosen address and based on insurance plans of interest. Specifically, this application would allow users to discover whether the provider is in- or out-of-network for each insurance plan of interest, the pricing of the provider’s services, and a rating based on quality and safety of care.

Prototype: Best Plan For You quiz

Another prototype we sketched out was a “What’s the best plan for you?” Quiz. After consumers input their demographic information, health information, and insurance needs/wants, this quiz returns a list of insurance plans that fits their needs. Both of these prototypes would simplify the shopping process by resolving the choice overload issue and enabling effortless comparison between plans.

The derivation trees that computable contract technology generates would be an essential part of developing the “What’s the best plan for you?” Quiz, and would make it easy to program the site to give an explanation of why certain plans are recommended. This technology could also be leveraged to create filters for the searching capability on the Healthcare Map since computable contract technology helps organize terms in a machine-readable script.

Prototypes: Cost Calculator & Chatbot

Finally, for the “Using a Plan” stage, we prototyped a Pre-Procedure Cost Calculator and a Logic-Programming based Chatbot.

For the Cost Calculator, consumers are able to fill out a page with information about a possible procedure, and then the tool will automatically inform the user about whether that hypothetical claim would be approved or denied.

If approved, the site will display the amount covered. If denied, it will produce a detailed explanation of why. This cost calculator would empower consumers to make decisions with the full knowledge of what the financial consequence will be.

In the case that consumers have more general questions, we formed the Chatbot prototype to help.

This application could either come in the form of an online chat or a phone call with automated responses. While this technology is already in place for many companies, computable contract technology would improve the bot’s capabilities because the heart of computable contracts is logic programming, which involves breaking down the policies into a set of rules and data, then creating an interpreter that can answer various questions.

The Pre-Procedure Cost Calculator can also be programmed entirely with logic programming as demonstrated by Professor Genereseth’s research. His work with the Codex team demonstrates the feasibility of computable contracts as he was able to create a Hospital Cash Claim form based on Chubb’s hospital cash product.

Results from first round of testing

We presented these five prototypes to six people during user testing sessions and asked them to rank each product based on the prompts: “Would you use this if someone offered it to you?”, “Would this help you get a better outcome with your health insurance?”, and “How easy would this be for you to use & understand?”

These testing sessions were done by recruiting of health insurance consumers from across the US through social media ads, like with our initial empathy interviews. Users were interviewed for around 20 minutes and compensated with $40 gift certificates.

Overall, people reacted enthusiastically to our ideas and the concept of using technology to improve consumer experience with insurance. The average rating for every product is over 4 out of 5.

What Next? Takeaways from Our Prototype Testing

For insurance companies, the user testing and research into the applications of computable contracts suggest that incorporating computable contract technology into a company’s services could streamline and integrate multiple processes.

For example, improving the chatbot would significantly cut down on human resource costs needed for answering people’s questions.

Another key example could be how having a program decipher whether a claim is approved or not would decrease time spent on each claim, allowing more time and money to be spent elsewhere.

For government agencies, our work indicates that requiring insurance companies to make their policies accessible through a computable contract would increase user satisfaction and empowerment.

The feasibility of a government branch mandating companies file machine-readable reports is supported by historical precedent. In 2009, the SEC required “corporations, mutual funds, and credit-rating agencies to report information in eXtensible Business Reporting Language (XBRL), a move that simultaneously reduced the costs of compliance for firms and cut the costs of accessing information for analysts, auditors, investors, and regulators.”

The same concept can be transferred from the finance industry to the insurance industry. Computable contracts then would also make it easier for the government agencies to tell if insurance companies are complying with guidelines because the information becomes easily accessible.

Our partners at CCIO highlighted many of the upcoming changes that could feed into this better future:

• Transparency in Coverage requirements, that go into effect on July 2022. Health providers will be required to publicly post their price information, in machine-readable formats. This can be used in computable contract tools to help consumers make smarter choices.
• The No Surprises Act (NSA) that will protect people against surprise medical bills. NSA will limit how much providers can charge, and what kinds of authorization are needed to get coverage. This might be useful when combined with a pre-procedure cost calculator.

Computable Contracts and the Law

The myriad potential applications of computable contracts in the health insurance space give rise to several legal issues.

Our insurance stakeholders provided us with the example of a chatbot using computable contracts to communicate policy coverage in layman’s terms. In that case, there was concern that if policy coverage were miscommunicated or misunderstood, the insurance company would expose itself to legal liability. Moreover, computable contracts must comply with data and privacy laws, which pose a particular challenge because they are widely applicable (EU data laws could apply to American insurers if they handle data from EU citizens), varying across many jurisdictions, and rapidly developing. In the following section, we will discuss some of the most important developments and their implications for computable contracts in the health insurance space.

This section will not cover data protection statutes related to health data, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA). The California Privacy Rights Act of 2020 (CPRA) exempts protected health information (PHI) that is collected by a covered entity and medical information governed by the California Confidentiality of Medical Information Act. However, because the CPRA exempts types of data and not types of entities, peripheral data collected by health insurers are still covered by California data laws.

Developments in Data & Privacy Law

The CPRA is at the forefront of the development of data and privacy laws in the United States. The law created the California Privacy Protection Agency (CPPA) which will begin enforcing the CPRA in July 2023. Major health insurers will almost certainly need to comply with the CPRA for data not covered by medical data laws; any business with more than $25 million in annual gross revenues is covered by the CPRA. Several of the rights granted to consumers in the CPRA were simply carried over from the California Consumer Privacy Act (CCPA) and so will be familiar to insurers. These include the right to: delete personal information, know categories and specific pieces of personal information, opt-out of the sale or sharing of personal information, and non-retaliation.

There are, however, new rights granted to consumers under the CPRA. These rights include rights to correct inaccurate information, limit the use and disclosure of sensitive personal information and opt-out of automated decision-making technology. Computable contracts will need to be able to comply with each of these rights. The right to opt-out of automated decision-making technology was primarily adopted to prevent companies from using machine learning to create consumer profiles, but it could severely limit the effectiveness of computable contracts if consumers are able and choose to exercise it in the computable contract context.

It is unclear to us from a technical standpoint whether these consumer rights will be a hurdle for or benefit of computable contracts. It is possible that computable contracts will make it easier to share information about data collection, make corrections and deletions, and offer opportunities to opt-out of data collection and sharing by better-integrating health insurance information into an insurer’s technology stack. However, it could also be the case that these rights would be difficult to square with computable contracts, in which case back-end use (as opposed to consumer-facing) of computable contracts would be preferable. Back-end applications of computable contracts in contract management would still have a large impact; Bain & Company estimates that improving contract management could save up to 100 basis points (1%) of companies’ revenue. However, it remains to be seen whether this benefit would flow to consumers.

For international insurers, there’s the additional privacy concern of compliance with GDPR and European Union Court of Justice (ECJ) decisions such as Schrems II. In Schrems II, the ECJ invalidated the EU-US Privacy Shield, which had previously allowed for the transfer of personal data from the EU to the US. Though there are currently negotiations for a new iteration of the privacy shield, any similar outcome will be likely to result in another adverse ruling — that is, a Schrems III — from the ECJ because the US seems unlikely to adopt a comprehensive federal privacy bill. As such, international insurers will need to localize their European data in Europe and keep it separate from their US data. This will limit some of the risk aggregation upsides of consolidated insurance data.

Other Legal Implications of Computable Contracts

A separate item for consideration is how judges will understand and apply contract law to computable contracts. On this matter, jurist comprehensibility will be important, especially if the computable contract is closer to code in application than to a natural language embodiment. Again, however, there will likely be a “legalese” counterpart to the computable contract that could alleviate this concern. If there are few operational issues with creating, translating, and managing these counterparts, this will be a viable solution.

That said, there could be a question as to whether electronic agents can even bind their principals through the decisions they make in smart contract code. As the Chamber of Digital Commerce notes, limited contemporary precedence exists on this question. On one hand, the automatic issuance of a tracking number was deemed “an automated, ministerial act” that did not constitute contractual acceptance in Corinthian Pharmaceutical Systems, Inc. v. Lederle Laboratories, 724 F. Supp. 605, 610 (S.D. Ind. 1989). On the other hand, the Tenth Circuit affirmed a district court finding of liability for an insurance company’s computerized reinstatement of an insurance policy, stating that “[a] computer operates only in accordance with the information and directions supplied by its human programmers. If the computer does not think like a man, it is man’s fault.” State Farm Mut. Auto. Ins. Co. v. Bockhorst, 453 F.2d 533 (10th Cir. 1972).

Possible Solutions

A way forward that could meet multiple stakeholders’ interests is a standardized computable contract template — akin to the ISDA Master Agreement for derivatives — for low-complexity, high volume health insurance contracts. The ISDA Master Agreement is a standard document created by the International Swaps and Derivatives Association (ISDA) used to govern over-the-counter (OTC) derivatives (i.e., derivatives traded directly between counterparties and not traded on an exchange). The ISDA Master Agreement standardizes terms for a derivative that can then subsequently be adjusted in a customized schedule. ISDA has been exploring possible applications of computable contracts since 2017. Moreover, since its most recent revision in 2002, the ISDA Master Agreement has developed a corpus of legal decisions that reduce uncertainty over legal matters involved in OTC derivatives.

Standardization would advance CCIIO’s interests by offering consumers fewer and clearer choices that limit cognitive overload and by providing a single template for regulators and watchdog groups to audit. Moreover, it would provide consumers with a body of policy claim decisions based on a similar contract, thereby reducing consumer uncertainty over what is likely to be covered.

Standardization would also advance insurers’ interests by creating a low-risk deployment area to implement and iterate the new technology involved in computable contracts. It would also present an opportunity for jurists and regulators to decide novel legal issues with computable contracts with less financial exposure for insurers.

Future Work

Our research shows that there is significant demand from consumers to improve information access and transparency in their experiences with health insurance, and that computable contracts pose many promising opportunities to alleviate these issues.

Future work that could stem from our research includes further user testing of our prototypes, as well as other computable contract applications, in order to determine which innovations have the most demand.

Another round of user testing might include testing our prototype ideas with a larger and diverse sample size of users to see which ideas are most promising and exciting to consumers. From there, we could create low-fidelity prototypes of the ideas our groups chooses to pursue, and then test various implementations with users to see which they prefer. It would be important for us to test these prototypes with a diverse group of consumers who all have varying insurance needs to ensure that we capture a variety of perspectives in our user research.

Should computable contracts become adopted by the insurance industry, there is also a need to create educational content and materials for judges and other legal professionals to utilize them effectively. One example of this would be training on how a judge can interpret the clauses of a computable contract since this will be quite different to interpret from a contract in document form.

Overall, we believe that there is potential for computable contracts to improve access and transparency in the insurance industry. As a result, insurance companies will benefit from efficiency improvements in their operations, consumers will have an improved experience and trust in insurance, and government agencies will be able to more effectively regulate insurance companies.

[1] Paez. K. et al. “Development of the Health Insurance Literacy Measure (HILM): Conceptualizing and Measuring Consumer Ability to Choose and Use Private Health Insurance”, in Journal of Health Communication, 2014.

[2] Loewenstein, G. et al. “Consumers’ misunderstanding of health insurance”, in Journal of Health Economics, 2013.

[3] Scitovsky, T. “Ignorance as a source of oligopoly power”, in Am. Econ. Rev. 40, 1950, 48–53.

[4] See Shaller, D. “Consumers in Health Care: The Burden of Choice”, Oakland, CA: California HealthCare Foundation, 2005; Wood, S., et al. “Numeracy and Medicare Part D: The Importance of Choice and Literacy for Numbers in Optimizing Decision Making for Medicare’s Prescription Drug Program” in Psychology and Aging, 2011, 295–307.

[5] See Centers for Medicare & Medicaid Services. Available online.

[6] See Health Insurance Marketplace, Understanding the Summary of Benefits and Coverage (SBC) Fast Facts for Assisters. Available online.

[7] See CodeX, Computable Contracts and Insurance: An Introduction. Available online.

The city of San Jose has developed an 8 point usability scale for its government websites.

These 8 steps lay out simple criteria for key factors to make a website work for visitors. These principles can also apply to legal aid, court, and other law help websites.

Our Legal Help Dashboard 4 key categories (tech performance, discoverability, content, and user-friendly design) subsume many of these individual 8 points. But it’s very useful to see them laid out!

Here are the alpha standards they propose for public interest website quality:

1. Easy to Use
2. Easy to Understand
3. Error-free
4. Mobile-friendly
5. Accessible
6. Consistently Designed
7. Fast
8. Discoverable

For legal help, we might add a few key other things on:

• Making jurisdiction very clear, so people aren’t relying on the wrong region’s laws
• Using content presentations that will be scannable, presentable on Google, and likely to convey complex legal issues
• Available in key languages other than English
• Built for people in high-stress, panic modes — so constantly offering phone numbers & easy access points in addition to the legal information

Margaret Hagan

LIST problem codes are standard ways to describe legal issues. How can you use them to make legal help better?

1. We need standard codes for legal problems.

There’s lots of different words we can use to describe the same legal problem. Is this thing an unlawful detainer, an eviction action, a landlord-tenant dispute, or getting kicked out of your house? These words come from legal jargon, different jurisdictions’ terminology, and people’s everyday language around the law.

This is a problem for building good legal help online.

To actually, efficiently connect people to help that fits their problem, we need standardized ways of referring to what these problems are. Taxonomy codes are one way to do this. If we have a standard, encoded term from a central taxonomy for each legal problem — then we can use this term to be standardized across websites, apps, and jurisdictions. Even if we use different words (eviction, u.d., kicked out) to call these problems, if we’re always using the same term (HO-02–00–00–00) when we talk about this problem online.

Our lab has spent several years, and with the support of the Pew Charitable Trusts and the Legal Services Corporation, to build this standard taxonomy of legal problem codes. It’s called LIST, the Legal Issues Taxonomy.

So how do you actually use these taxonomy codes?

How do you make these codes work for you and your users?

2. Web administrators can tag their help resources with the LIST codes.

If you run a website that offers legal help — like a legal aid site, a law library, a court help center, or otherwise — you can use these LIST codes to make it easier for people to find your help resources. And you can make it easier for other help providers to link to your resources.

The best way to use the LIST taxonomy codes is to put them in Schema.org markup, that describe your websites’ topics to search engines. You can do that by using this Schema markup creator tool that our Lab has created.

You can make this Schema markup, and then paste it into your website’s header. This markup will describe your legal help organization and the issue areas that you cover. The form automatically puts the right LIST code in for the issue area you select on the page.

Or, you can manually create your own Schema markup using LIST codes. You can use the Schema term KnowsAbout, and then populate it with the LIST codes that represent the legal problems your organization can help people with.

3. App & bot developers can use LIST codes to encode people’s inputs and their responses.

If you build bots, conversational agents, and other apps that go back-and-forth with users, then the LIST taxonomy codes can help you tag what people are asking for help with. And you can similarly encode the resources and links you’re offering to your users.

For example, our Lab’s Eviction Help Bot can spot people’s legal problems around possible evictions and landlord-tenant. It does this by running people’s posts on the Reddit/LegalAdvice forum through the SPOT tool from Suffolk LIT Lab.

We have SPOT read the post and then determines if one of the LIST legal problem codes seems to be present. If a LIST problem code around eviction probably applies, then the Eviction Help Bot replies to that Reddit-user with an automatic message about websites that could help them.

When we were building that bot, we programmed which LIST problem codes the bot should be sensitive to. Other bot- or agent-developers can use the LIST codes similarly.

1. Set up SPOT account and connection. You can set up an account and a way for your tool to make calls to SPOT through its API,
2. Use SPOT to make sense of your users’ text: You pass a piece of user-generated text (a search query, a paragraph description of a problem, a transcription of an intake call) over for SPOT to categorize, and then
3. Get the LIST problem codes back from SPOT: You have SPOT return the LIST issue codes that seem to be present.
4. Have your tool use the codes to reply intelligently: You have your bot or agent respond accordingly. You have automated responses or links associated with certain LIST issue codes. If eviction issue codes are returned, then have the bot reply with links to eviction help resources. If divorce issue codes are returned, then you can have an automatic message on the divorce process.

4. Referral link to other groups’ webpages & contacts based on their LIST-encoded expertise

This last use case is a more ambitious one. It requires multiple legal help groups to coordinate using the LIST problem codes.

Let’s say that you run a lawyer referral system, and want to better match people calling in to a lawyer that can help them with a specific problem.

Or let’s say that you run a legal help website, and want to be able to direct visitors to the right online resources for the problem they have. Maybe your group’s website has self-help materials on debt, wage theft, and repossession. But you don’t have many materials on protective orders, divorce, custody, or other family law matters.

And maybe you serve clients in southern Ohio, but not in central or northern Ohio (or from other states or countries). You don’t want to be giving incorrect legal procedure guidance to visitors. You want to make sure they find local-jurisdiction help.

LIST codes could help you make better hand-offs for your web visitors or people calling into your referral line. If you have used LIST to encode legal help websites’ expertise topics, or lawyers’ expertise areas, then you can easily find the right next step for the person.

If other legal help groups are using LIST codes to describe the issues they have resources on, and the jurisdictions they serve — then you could use this information to tell your visitor which website they should be visiting to get issue- and jurisdiction-correct help.

This would require making a referral database system using the LIST codes and jurisdiction codes, and then having a search/input function on your website. When your visitor searches for a certain issue/location, or inputs a story about an issue/location, then your site can draw from the referral database to pass the visitor off to the best website for them.

We haven’t yet built this kind of intelligent referral system — but we know others are starting to. This can include through steps like:

• Having lawyers or legal groups all report what issue areas they can help with, or that they have self-help resources on. Ideally, this would be on a structured form, with a drop-down or multiple choice list of issue area options. These submissions should all be encoded with standard LIST problem codes. Then this will make it easier to search for and identify which lawyers/websites could be good matches for a specific person.
• Creating a master-list of legal help websites, and which URL pages have help for certain issue areas. This entails collecting a database/spreadsheet of main website pages, as well as specific sub-pages that have help for certain problem areas. Each page’s listing should have fields that describe the LIST problem codes present on the page, and the jurisdiction code for what it applies to. Then people and tech tools can draw from this master-list to find the local, issue-specific page to refer people to. You can see the start of such a master-list here.

Do you have other use cases for using LIST problem codes, or other taxonomies in your justice innovation work?

Please let us know! We believe in the power of standards and coordination.

This piece was originally published on the Legal Design Lab’s main page in May 2021.