HxGuide

COMPUTER - ASSISTED HISTORY TAKING SOFTWARE (CAHTS)

Incomplete history taking is the most important factor contributing to diagnostic error in a teleconsult. Research has shown that completeness in history taking and physical examination in virtual visits varied from 51.7% to 82.4% based on the telemedicine provider. History taking in currently available teleconsultation models is routinely rushed or incomplete due to time pressures as well as because of not being able to see the patient. Simple relevant questions related to history taking, including allergies, medications or pregnancy status, are routinely missed resulting in missed diagnoses. Documentation of a clinical encounter is also poor as the teledoctor may not have time to be able to enter the patient information into the medical record. As a result, telemedicine providers find it challenging to monitor the quality of their programs.

Inability to perform a physical exam is a well-known limitation of telemedicine. Hence, in the case of conditions that require an in-person physical exam such as a patient presenting with abdominal tenderness with a possibility of lumps, telemedicine is not adequate to comprehensively assess the patient’s condition and referral to an in-person visit must be facilitated. This may not be feasible in a low resource setting. In order to minimize the patient’s need to travel to access care, more care tasks must be performed by the FHW. This task-shifting of care processes from doctors to health workers in a telemedicine setting is an important need.

A CAHTS can be used to task-shift the process of clinical data collection from a doctor to a health worker, thereby allowing better quality of information to be collected and documented in a resource limited setting. This would result in an overall improved quality of the primary care consult. In this consult, the doctor has to take a patient’s history of presenting illness, perform a physical exam and should also collect the patient’s family history, past medicine history, social history occupational history, etc. Due to enormous time constraints on doctors in a rural care setting, the quality of information gathered is often poor. In telemedicine, this problem is amplified since the doctor is not in the same location and cannot physically see, touch or hear the patient. The community based health provider serves as the “hands and eyes” of the doctor in the field. The frontline health worker (FHW) does not have the required training or skill to be able to collect this information. Hence most telemedicine programs involve the FHW simply registering the patient with the remaining history being taken by the doctor over the phone or over video consult.

The Intelehealth platform uses a knowledge engineered computer-assisted history taking guide, to ensure that health providers gather comprehensive clinical history and conduct clinically relevant physical exams. The app independently guides the user through a series of questions and exams based on the patient’s presenting symptoms and medical history. Additionally, it provides interactive job aids that help community-based providers during the physical exam, such as video, pictures, and sounds when relevant to the question being presented. Currently the knowledge engine codifies 67 common complaints & 143 physical exams; this knowledge has been developed through the collaborative efforts of a panel of experts and clinicians following evidence based clinical guidelines. The data generated from these protocols is easily searchable, analyzable providing rich data for telemedicine program administrators to monitor the quality, safety and efficacy of consults.

Creating mind maps using FreeMind

In order to visually "draw" (program) these protocols in HxGuide we use a free open source mind mapping tool called FreeMind.

A sample flowchart and the generated history note for a single presenting complaint for “Abdominal Pain” is shown below:

The presenting symptom is the "root node" of the mind map. For each complaint, there is one mind map - the protocol collects data on ‘dimensions’ of the presenting symptom such as the site of the pain, radiation, duration, onset, timing of day when pain is experienced, nature of the pain, severity, associated symptoms, exacerbating factors, relieving factors and (for women) menstrual history. These dimensions are child nodes of the complaint root node. Each dimension has associated ‘clinical findings’ – each clinical finding is an atomic data point - which are further depicted as child nodes. Triggers are programmed at the node level to describe and extend the behavior of the node eg: defining the input data type for a ‘clinical finding’. These are called ‘attributes’. The tool also allows for management of the translation of all questions, answers and prompts for findings and dimensions into different languages using ‘display’ attributes for each node. Finally, as the user is guided through the protocol and enters information, the gathered information is collected into a succinct history note in natural language through the ‘language’ attribute that inserts punctuation, phrases to ensure grammatical correctness and bullets to make it easily readable.