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Grand Challenges in Dental Informatics

¹ Clinical Informatics Research Network, Kaiser Permanente, Portland, OR;
² Northwest Permanente P.C., Physicians and Surgeons, Portland, OR;
³ Center for Health Research, Kaiser Permanente, Portland, OR;

Correspondence: ^* corresponding author, Center for Health Research, 3800 N. Interstate Ave. (CHR @ WIN), Portland, OR 97227, Dean.F.Sittig{at}kp.org

	Abstract

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
When truly significant scientific challenges are overcome, it profoundly changes the daily activities, as well as the future research activities, of everyone involved in the related field. By identifying and describing the grand challenges facing a scientific field, we can help funding agencies identify and prioritize projects for support, stimulate and encourage new investigators to work on these intellectual and technological challenges, and help define the field itself. In this article, we present an informatics-oriented, future-patient-care scenario, then describe a series of applications and the related informatics grand challenges facing the dental field today. New techniques and technologies to help us overcome these challenges would facilitate the development of truly monumental applications, such as a comprehensive electronic oral health record, an automated dental treatment planning system for all diagnoses, or a system to profile patient risk for chronic oral diseases.

KEY WORDS: Dental informatics • dentistry • medical informatics • research

	Introduction

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
Dental informatics is the "application of computer and information science to improve dental practice, research and program administration" (Eisner, 1999). Many current dental informatics applications are classified as such because they combine computer technology with high-tech devices for a uniquely dental use—for example, intra-oral imagery, laser handpieces, and office management systems. However, it is the data and information that are captured and manipulated that fall more directly into the realm of dental informatics.

Therefore, we define the grand challenges facing the field of dental informatics as specific scientific and information technology innovations that would remove critical barriers to solving important problems in dentistry. If these grand scientific challenges were overcome, it would profoundly change daily practice, as well as the future research activities, of everyone in the dental field. We are particularly concerned with those concepts that are likely to make a significant positive impact on the field and those that have a high degree of feasibility. Identification of these grand challenges can help researchers and research sponsors to focus their activities on truly important clinical and technical problems.

To illustrate what is needed in the field of dentistry and where we want to go, we present one future-patient-care scenario. The goal of this scenario is to identify key functions, applications, or technologies in the field of dental informatics. Following this scenario, we summarize these concepts and list the grand challenges in the field.

	Possible Future-patient-care Scenario

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
Now that the kids are grown and on their own, Frank and Gloria recently moved to a smaller town out of state. Getting established and finding a doctor and dentist were top priorities, especially since Gloria has diabetes and was told by her last dentist that she has ‘bad gums’ and should be under the regular care of a dentist. On top of that, her mouth is beginning to bother her.

Thanks to help from their children, Frank and Gloria have decided to use the Internet to help them find a dentist. Gloria’s neighbor recommended Dr. Brown. Gloria decided to find out all she could about Dr. Brown, so she went to the Internet and found Dr. Brown’s Web site. The Web site told about Dr. Brown’s office and staff as well as Dr. Brown’s background and his philosophy of dental care.

Gloria and Frank liked what they saw and decided to schedule an appointment. They used the online registration and appointment scheduler on Dr. Brown’s Web site. During the registration process, Gloria was asked about her previous dental treatment and medical history. To give Dr. Brown accurate information, Gloria logged into her Personal Electronic Medical and Oral Health Record and added Dr. Brown to the authorization list. Since Dr. Brown was new to the list, the computer system used its artificial intelligence to determine which parts of Gloria’s record would be made available to Dr. Brown. Gloria approved the release of the protected information, which gave Dr. Brown additional privileges to view previously restricted data.

Two days before her appointment, Gloria received an e-mail welcoming her to Dr. Brown’s practice and confirming the appointment. She was also given a user ID and password to the private and confidential "new patient" portion of Dr. Brown’s Web site. Gloria logged on and ‘participated’ in a new-patient pre-visit interview with an avatar on the computer. Gloria was asked about her expectations for the upcoming visit, her past and current personal dental experiences, as well as her preferences and attitudes toward dental care and dentistry in general. She also discussed her family situation and new environment.

Back in Dr. Brown’s office, a new-patient report had been automatically generated that combined data from Gloria’s registration, pre-visit interview, and previous medical and dental records. Using Gloria’s universal ID and his own authenticated dentist ID, Dr. Brown logged onto Gloria’s electronic health record and reviewed the past five-year history of Gloria’s full-mouth x-rays, which included an analysis of the changes in alveolar bone height and density, and an assessment of activity of caries lesions.

When Gloria arrived for her first visit, she was welcomed by Dr. Brown and spent a few minutes talking about her concerns and expectations. Dr. Brown, having previously reviewed Gloria’s demographic profile, was able to focus on many personal pertinent points. The oral exam followed the introductory consultation.

During the oral exam, various digital techniques were used to record x-ray and visual images of Gloria’s hard and soft tissues as well as to record jaw and occlusal relationships. Dr. Brown conducted salivary, crevicular fluid, and other protein microassays using nano-robots and nano-molecular sensors, which transmitted results to a remote database that combined Gloria’s medical and oral information. Analysis and interpretations were bounced back in summary form to Dr. Brown’s decision support system. The summary report provided an immunological profile, relevant protein and polypeptide markers, and genetic and other pre-disposition biomarkers. Biomarkers were correlated with genomic data to help map and match Gloria’s potential risk for oral diseases, especially caries and periodontal disease. As it turned out, Dr. Brown discovered an abnormality in her crevicular fluid analysis. The genetic testing indicated a predisposition for breast cancer—BRCA-1 was noted. Dr. Brown made an electronic referral and an automatic test result transmission to Gloria’s physician.

Dr. Brown used automatic speech recognition to enter information into Gloria’s record. Based on a universally accepted and standardized controlled vocabulary, the intelligent system automatically coded physiologic state, signs, symptoms, and personal characteristics, which listed potential diagnoses. Because Dr. Brown’s Electronic Oral Health System was linked and integrated with the most current evidence-based clinical dental and medical guidelines, a risk profile of Gloria’s current oral health status was generated and reported to Dr. Brown. For example, Gloria’s diabetic condition puts her at increased risk of many dental diseases, particularly periodontal disease.

The integrated treatment planning system provided evidence-based information and proposed multiple treatment options tailored to Gloria’s medical and dental status, personal preferences, health risk profile, and financial constraints.

Using visual displays of the treatment planner, Dr. Brown and Gloria reviewed and discussed the pros and cons of each treatment option. To help Gloria better visualize what the final appearance would be, Dr. Brown used a 3-D imaging system to show Gloria ‘before and after’ pictures. Since some of the treatment involved modification to Gloria’s smile, Dr. Brown and Gloria simulated multiple outcomes based on Gloria’s preferences. Dr. Brown was also able to give Gloria information on the likely outcomes for differing treatment options based on a national database of similar procedures performed on people like Gloria.

Before Gloria left, Dr. Brown gave her an Information Therapy Prescription so that she and Frank could gather additional reliable information from the Internet about the various treatment options and the nature of her dental health. In addition, all of the information that had been gathered during the examination was made available to Gloria in her personal dental record. Available through Dr. Brown’s Web site was a library of vignettes of people whom Dr. Brown had treated. These people had previously given Dr. Brown permission to videotape interviews about their personal experiences and have prospective patients e-mail them for permission to view the vignettes. Gloria e-mailed a few of the patients who had similar treatment. Two of them agreed to talk with Gloria and allowed her to review the video interviews.

Together, Gloria, Frank, and Dr. Brown decided on a treatment plan, which considered the evidence on relevant outcomes and Gloria’s preferences and values, as well as what they could afford to pay after her insurance benefits were calculated. The treatment was estimated to take six months.

During the treatment, Dr. Brown wanted to monitor Gloria’s oral condition very closely, so he inserted a microscopic robotic sensor into Gloria’s mouth to monitor and report oral conditions and functional activity. The sensors processed the data and sent a report to Dr. Brown’s Electronic Oral Health Record (EOHR). These reports provided an ongoing quality assessment of the treatment plan chosen.

A part of Gloria’s periodontal treatment involved a bone augmentation procedure. Dr. Brown wanted to discuss the treatment with a colleague who lived across town. They relied on teleconsultation technology to review Gloria’s case. Dr. Brown knew that Gloria was diabetic, and he wanted specific information about gene probe analysis and sensor detection to better assess Gloria’s risk level. Dr. Brown’s clinical decision support system was intelligent enough to communicate with the intra-oral sensors. These sensors measured her blood glucose, saliva, and crevicular fluids and reported back to Dr. Brown the shifts in glucose tolerance due to metabolic stress of infection related to periodontal disease and bone augmentation procedures. This information helped Dr. Brown better manage Gloria’s medications and clinical care.

The bone graft material he wanted to use for Gloria was experimental and part of a large-scale clinical trial. After he explained the purpose of the study to Gloria and obtained her consent, the data were automatically forwarded to the study coordinator. Gloria was flagged for a follow-up visit to determine effects of using this new material.

To ensure the maximum opportunity for success of the surgical procedure, Dr. Brown took advantage of the advanced dental surgical simulation theatre available through a university center of excellence Web site. Dr. Brown logged onto the center’s Web site and entered his ID as well as Gloria’s ID number. The remote system was able to simulate the bone augmentation procedure and ‘walked’ Dr. Brown through the surgical steps. What was most beneficial to Dr. Brown was being able to use the 3-D simulations, based on Gloria’s own images, with a haptic feedback apparatus, to increase his sense of how the materials could be manipulated intra-orally and placed in the defect, and to try different suturing and closing alternatives.

All of the information collected during Gloria’s examination, diagnosis, and treatment was automatically transmitted to, and became part of, her Personal Health Record. When Gloria’s treatment was completed, Dr. Brown submitted the case and all supporting documentation for inclusion in a national dental treatment database.

Based on detailed physiologic, psychosocial, and environmental information obtained throughout her care, a long-term self-care and self-management maintenance and prevention program was presented to Gloria by Dr. Brown as part of the EOHR post-treatment summary and recommendations. The self-care guidelines incorporated into the EOHR are evidence-based and use artificial intelligence to format and present a self-management program tailored to Gloria’s personal preferences, cognitive learning style, physiologic state, and readiness to change. Reminders and recommendations for Gloria’s prevention and maintenance program are time-linked and automatically incorporated into her personal Web page as well as ‘calendared’ in her wireless personal agent that is linked to and integrated with her digital cell ‘phone assistant and home-based digital secretary. Finally, motivational video clips from Dr. Brown are sent to Gloria over the next six months as part of her ongoing care.

	Mapping the Scenario to Specific Informatics Applications

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
Clearly, developing all the technologies required to make this scenario possible will require researchers and dental professionals to address multiple grand challenges. The following applications leverage the foundations of dental informatics, which are more clearly defined in the next section.

The following applications, processes, and technologies would need to be designed, developed, and implemented:

(1) A system could profile patient risk for specific chronic oral disease status (e.g., chronic periodontitis, temporomandibular joint dysfunction, or caries) based on a few variables (age, gender, current oral status, number of carious teeth, tobacco use, xerogenic medications, etc.). The system would rely on outcome data from a large cross-section of the population to generate its predictions.

(2) A population-based system could assess the likelihood of successful outcomes for dental treatments such as implants, periodontal surgery, prostheses, amalgam, plastic, cast, and ceramic restorations. The same system could also tailor prevention recommendations to have a high probability of uptake and success based on a holistic profile (i.e., genetic, physiologic, psychosocial, familial, and environmental characteristics of the patient).

(3) 3-D image manipulation and simulation systems could illustrate the effect on the current patient’s appearance of specific proposed procedures, such as maxillofacial surgery, orthodontics, periodontal grafts, and cosmetic dentistry. Such a system would also help clinicians incorporate and normalize the expectations of the patient, dentist, and parent (if the patient is a child).

(4) An automated treatment planning system for all dental diagnoses could be used by clinicians (evidence-based guidelines for dental diseases and conditions, multiple semi-edentulous conditions, and malocclusions). Such a system should be based on the best available evidence from clinical trials. When such evidence does not exist, the system should be able to extract similar cases from a nationwide diagnosis and treatment outcome database to identify best practices (based on expert opinion, consensus, and other less robust evidence). An automated treatment planner would be similar to a clinical decision support system (guideline-based) for clinicians. It would help them present appropriate treatment options to specific dental diagnoses tailored to patient characteristics, health states, and preferences.

(5) A decision aid for patients would help them learn about all personally relevant and applicable dental treatment options. Such a support system would also help patients in a decision-making process that would lead to a value-based choice among the various alternatives. The aid would empower patients to become more engaged in the decisions surrounding their care, as well as in preventive health practices.

(6) Procedures and processes would be needed to capture and analyze digital images progressively over time to track hard-tissue changes. Special software would be needed to compensate for sources of error, e.g., radiographic technique artifacts in exposure and beam angulation variations. While most information pertains today to radiographic imaging, this field will likely grow to encompass Quantitative Laser Fluorescence (QLF) and other quantitative approaches.

(7) Nano-robots would be used intra-orally to monitor and transmit information on various markers of oral health status, such as salivary pH fluctuations and long-term trends, degree of mineralization shifts at certain index tooth surfaces, salivary enzyme and bacteriologic make-up changes over time, and information about crevicular fluid flow and composition. The results reported by the nano-robot would be recorded in a patient-specific database and used to monitor and alert the care provider to alterations that point to precursors of disease.

(8) Computer-based simulations could incorporate haptic feedback and thus allow students to develop and enhance their fine motor skills.

	Overview of Informatics Grand Challenges

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
To consider the design and development of any of the applications listed above, one must have a core set of information and knowledge. The acquisition, classification, storage, and retrieval of this information must precede the realization of any of the envisioned applications. The grand challenge of dental informatics is not to dream up possible applications that benefit and move dentistry into the information age; rather, it is to figure out what information to collect, how to collect it, and then how to use it. Looking at a view from 50,000 feet, rather than from the clinicians’ chair-side perspective, we see the following as challenges that must be addressed:

(1) To develop a knowledge-based ontology of dental concepts from which one could extract a standardized controlled clinical terminology to describe dental signs, symptoms, conditions, diseases, and treatments (i.e., procedures, methods, techniques, materials, and devices). Such an ontology forms the basis of the field of dental informatics. This ontology must: contain elements, such as permanent clinical concepts and non-semantic identifiers; allow for a polyhierarchy; include formal definitions; not allow "not elsewhere classified" terms; use multiple granularities; and allow for recognized redundancy (Cimino, 1998; Rector, 1999). Without such a standardized controlled terminology, all other clinical data and knowledge bases will not be of much use.

(2) To develop an evidence base of etiology, diagnosis, prevention, treatment, and treatment outcomes (including materials, methods, techniques, and usage) for a large proportion of dental patients and dental practices. Such a knowledge base would require tracking of: genomic, psychosocial, and physiologic dental health markers; prevention regimens; materials; techniques; treatments; outcome results; side-effects; and environmental influences (local and global). Some of the key informatics contributions to such an effort would include the development of large, integrated clinical databases of de-identified patient data (Tierney and McDonald, 1991; Hayden, 1997).

(3) To develop a comprehensive electronic oral health record that is seamlessly integrated into the automated medical record. Such a system would consist of a database of patients’ health-related information entered by any healthcare worker. It would allow clinicians to document findings and plans, provide links to online information resources, facilitate real-time clinical decision support, and facilitate the transmission of information to other clinicians.

(4) To develop a nationwide oral health database that contains basic patient-level diagnostic, treatment, and outcome data linked to a nationwide medical database (Heid et al., 2002; ANSI, 2003). Such a resource would allow dentists to identify relationships between and among dental diseases and conditions, and medical diseases, conditions, and medications.

(5) To automate data capture, integration, and synthesis to create real-time, knowledge-based, clinical monitoring systems based on both continuously and intermittently available analog and digital data. Such systems must include data from multiple input sources of widely varying degrees of accuracy and reliability. These monitors will require the development of robust algorithms for information and knowledge processing and must also be capable of determining how, when, and whom to notify in the event of the detection of a significant clinical event.

(6) To develop learner-centered educational systems that select a learning goal, evaluate the student’s abilities, and determine the individual learning style (Clancy et al., 2002). They could then create a structure that is tailored to offer educational material in the most effective way.

	Political and Social Obstacles

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
Before we can start tackling any of the grand challenges identified, we must caution researchers and research sponsors alike that any of the following obstacles could derail even the most creative information management solutions.

Universal patient identifier
To link patient records across facilities, there must be some means of accurately and uniquely identifying each patient. While researchers have developed systems that use a combination of patient identifiers (Grannis et al., 2002), a single, unique, nationally assigned patient identifier would significantly improve our ability to link patient records (Freriks, 2000).

Secure User authentication
Most of these solutions will require a secure, easy to use, difficult to falsify, method of authenticating users. While biometric identification schemes abound, they are still very difficult to use and are often unreliable.

Universal access to computers and high-speed Internet connections
Access to computers with high-speed Internet connections must increase for both patients and providers. The so-called "digital divide" will continue to hamper all efforts to develop these advanced systems.

National list of authorized dental practitioners
Many of these systems require a means to uniquely and accurately identify and authenticate that the user is an authorized clinician. Currently, no lists of all healthcare workers are nationally maintained.

Collections of large databases of patient information
Many of these systems will require that large collections of patient information be developed. To date, there has been a tremendous public outcry against such efforts.

	Summary and Conclusions

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
Dental informatics is a relatively new field that is facing significant challenges. The hypothetical patient-care scenario, coupled with the specific informatics-related applications, illustrate the grand challenges facing the field of dental informatics today. If we can agree on a set of challenges facing the field, then we can begin to develop solutions to address these challenges.

	Footnotes

 
Publication supported by Software of Excellence (Auckland, NZ)

	References

TOP Abstract Introduction Possible Future-patient-care... Mapping the Scenario to... Overview of Informatics Grand... Political and Social Obstacles Summary and Conclusions References

 
ANSI (2001). ANSI/ADA Specification No. 1000: Standards Clinical Data Architecture for the Structure and Content of an Electric Health Record (http://www.ada.org/prof/resources/positions/standards/informatics.asp#1000).

Cimino JJ (1998). Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med 37:394–403.[Medline]

Clancy JM, Lindquist TJ, Palik JF, Johnson LA (2002). A comparison of student performance in a simulation clinic and a traditional laboratory environment: three-year results. J Dent Educ 66:1331–1337.[Abstract]

Eisner J (1999). The future of dental informatics. Eur J Dent Educ 3(Suppl 1):61–69.

Freriks G (2000). Identification in healthcare. Is there a place for Unique Patient Identifiers? Is there a place for the Master Patient Index? Stud Health Technol Inform 77:595–599.[Medline]

Grannis SJ, Overhage JM, McDonald CJ (2002). Analysis of identifier performance using a deterministic linkage algorithm. Proc AMIA Symp :305–309.

Hayden WJ (1997). Dental health services research utilizing comprehensive clinical databases and information technology. J Dent Educ 61:47–55.[Abstract]

Heid DW, Chasteen J, Forrey AW (2002). The electronic oral health record. J Contemp Dent Pract 3(1):43–54.[Medline]

Rector AL (1999). Clinical terminology: why is it so hard? Methods Inf Med 38:239–252.[Medline]

Tierney WM, McDonald CJ (1991). Practice databases and their uses in clinical research. Stat Med 10:541–557.[Medline]

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