Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system

<p>Abstract</p> <p>Background</p> <p>The CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina.</p> <p>Methods/Design</p> <p>The family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome).</p> <p>Discussion</p> <p>This study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments.</p

Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system

Background: The CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina. Methods/Design: The family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome). Discussion: This study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments

Evidence-based guidelines and decision support services: a discussion and evaluation in triple assessment of suspected breast cancer

Widespread health service goals to improve consistency and safety in patient care have prompted considerable investment in the development of evidence-based clinical guidelines. Computerised decision support (CDS) systems have been proposed as a means to implement guidelines in practice. This paper discusses the general concept in oncology and presents an evaluation of a CDS system to support triple assessment (TA) in breast cancer care. Balanced-block crossover experiment and questionnaire study. One stop clinic for symptomatic breast patients. Twenty-four practising breast clinicians from United Kingdom National Health Service hospitals. A web-based CDS system. Clinicians made significantly more deviations from guideline recommendations without decision support (60 out of 120 errors without CDS; 16 out of 120 errors with CDS, P<0.001). Ignoring minor deviations, 16 potentially critical errors arose in the no-decision-support arm of the trial compared with just one (P=0.001) when decision support was available. Opinions of participating clinicians towards the CDS tool became more positive after they had used it (P<0.025). The use of decision support capabilities in TA may yield significant measurable benefits for quality and safety of patient care. This is an important option for improving compliance with evidence-based practice guidelines

Early breast cancer sharing the decision: a critical appraisal

Great debate surrounds the issue of patients with breast cancer participating in surgical/medical decision making and their ability to give an informed consent. Health care professionals must balance the need to safeguard the rights of patients, respect their autonomy and yet be sensitive to the changes and individual variations a patient may demonstrate as they progress from diagnosis to the end point of their disease. The premise underpinning the study and literature review, reflected in the published works presented here, focuses on a woman's right to access, should she choose, accurate information to make an informed treatment choice based on an exploration of the literature which reviews the ethical issues including autonomy, informed consent, advocacy, communication, access to information, approaches to shared decision making, psychiatric morbidity and evidence based medicine. Objectives of the Study Reflected in the Published Work Presented Here: 1. To determine the acceptability of an interactive video system, in addition to the standard informational care and support provided by the clinicians and clinical nurse specialist, as a means of providing information about the risks and benefits of treatment choices-surgery and subsequent adjuvent chemotherapy - to women with early breast cancer who are facing choices about treating their early breast cancer. 2. To determine whether providing information to women with early breast cancer using an interactive system significantly reduces anxiety and depression associated with the diagnosis and treatment of this condition. 3. To determine whether providing information using an interactive video system, to women about treatment choices significantly increases patient satisfaction with the choice they have made. To assess this for a two year period patients attending for surgical treatment for early breast cancer were recruited, after full discussion and written consent, into a randomised control trial to evaluate the acceptability and effectiveness of the interactive video system. Eligible patients (100)included all women with an early primary invasive breast cancer who had a genuine choice between treatment options.. Patients excluded from recruitment and viewing the Interactive Video (IVD)/Shared Decision - Making Programme (SDP) were all women who did not have a straightforward choice. All patients in the intervention group completed the following: a. Acceptability of the Interactive Video; b. Assessment of Health Status, The SF36 (Ware and Sherbourne 1992) c. The Hospital Anxiety and Depression (HAD) scale (Zigmond & Snaith 1983) After nine months the patients were again asked to complete the three questionnaires but at this point Questionnaire 1. elicits the patient's satisfaction with their treatment choice

Breast cancer survival analysis agents for clinical decision support.

peer reviewedPersonalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study

A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201