Informatics_NN1 Methods_NN2 to_TO Enable_VV0 Patient-centered_JJ Radiology_NN1 Patient-centered_JJ radiology_NN1 is_VBZ a_AT1 topic_NN1 of_IO increasing_JJ importance_NN1 in_II radiology_NN1 ,_, motivated_VVN by_II both_RR physicians_NN2 and_CC patients_NN2 ._. 
From_II the_AT physician_NN1 perspective_NN1 ,_, patient-centered_JJ radiology_NN1 is_VBZ being_VBG driven_VVN by_II the_AT rapidly_RR evolving_VVG medical_JJ and_CC molecular_JJ knowledge_NN1 that_CST promises_VVZ personalized_JJ medicine_NN1 ._. 
Patients_NN2 are_VBR also_RR driving_VVG patient-centered_JJ radiology_NN1 ,_, with_IW increasing_JJ expectations_NN2 of_IO accessing_NN1 and_CC reviewing_VVG their_APPGE medical_JJ information_NN1 and_CC participating_VVG more_RGR actively_RR in_II their_APPGE care_NN1 ._. 
Patient-centered_JJ radiology_NN1 practice_NN1 is_VBZ crucial_JJ ,_, affecting_VVG the_AT visibility_NN1 of_IO radiologists_NN2 in_II health_NN1 care_NN1 ._. 
The_AT patient-centered_JJ radiology_NN1 paradigm_NN1 brings_VVZ important_JJ challenges_NN2 to_II both_RR physicians_NN2 and_CC patients_NN2 ._. 
From_II the_AT physician_NN1 perspective_NN1 ,_, two_MC important_JJ challenges_NN2 are_VBR selecting_VVG the_AT appropriate_JJ imaging_NN1 procedure_NN1 for_IF specific_JJ patients_NN2 and_CC rendering_VVG the_AT best_JJT imaging_NN1 interpretation_NN1 that_CST is_VBZ personalized_VVN to_II the_AT particular_JJ patient_NN1 (_( eg_REX ,_, by_II incorporating_VVG historical_JJ and_CC clinical_JJ data_NN )_) ._. 
In_II selecting_VVG an_AT1 imaging_NN1 procedure_NN1 for_IF specific_JJ patients_NN2 ,_, radiologists_NN2 have_VH0 an_AT1 increasing_JJ number_NN1 of_IO choices_NN2 ._. 
There_EX are_VBR many_DA2 new_JJ diagnostic_JJ agents_NN2 and_CC imaging_VVG techniques_NN2 ,_, increasingly_RR tailored_VVN on_II the_AT basis_NN1 of_IO patient-specific_JJ information_NN1 ._. 
Radiology_NN1 is_VBZ evolving_VVG from_II generic_JJ imaging_NN1 protocols_NN2 for_IF broad_JJ categories_NN2 of_IO indications_NN2 to_II specific_JJ ,_, often_RR complex_JJ ,_, image_NN1 acquisition_NN1 protocols_NN2 designed_VVN to_TO answer_VVI specific_JJ clinical_JJ questions_NN2 ._. 
Radiology_NN1 procedures_NN2 and_CC the_AT selection_NN1 of_IO imaging_VVG agents_NN2 will_VM be_VBI increasingly_RR customized_VVN according_II21 to_II22 each_DD1 patient_NN1 's_GE underlying_JJ or_CC suspected_JJ disease_NN1 process_NN1 ,_, particularly_RR in_II the_AT era_NN1 of_IO molecular_JJ imaging_NN1 ._. 
Rendering_VVG the_AT best_JJT patient-specific_JJ imaging_NN1 interpretation_NN1 is_VBZ another_DD1 requirement_NN1 for_IF radiologists_NN2 in_II the_AT patient-centered_JJ radiology_NN1 paradigm_NN1 ._. 
The_AT pace_NN1 of_IO discovery_NN1 in_II radiology_NN1 is_VBZ rapid_JJ ,_, and_CC radiologists_NN2 are_VBR challenged_VVN by_II the_AT information_NN1 explosion_NN1 and_CC their_APPGE ability_NN1 to_TO keep_VVI pace_NN1 with_IW the_AT latest_JJT knowledge_NN1 that_CST could_VM affect_VVI their_APPGE imaging_NN1 interpretations_NN2 ._. 
Tools_NN2 are_VBR needed_VVN to_TO help_VVI radiologists_NN2 access_NN1 and_CC use_VVI current_JJ knowledge_NN1 ,_, to_TO guide_VVI them_PPHO2 in_II customizing_VVG imaging_NN1 to_II each_DD1 patient_NN1 ,_, and_CC to_TO help_VVI them_PPHO2 render_VVI the_AT most_RGT accurate_JJ diagnoses_NN2 ._. 
From_II the_AT patient_JJ perspective_NN1 in_II patient-centered_JJ radiology_NN1 ,_, the_AT expectation_NN1 is_VBZ that_CST they_PPHS2 will_VM assume_VVI active_JJ roles_NN2 in_II their_APPGE care_NN1 ,_, particularly_RR being_VBG informed_VVN about_II the_AT results_NN2 of_IO imaging_VVG procedures_NN2 and_CC participating_VVG in_II medical_JJ decision_NN1 making_VVG after_II receiving_VVG those_DD2 results_NN2 ._. 
Patients_NN2 ,_, like_II physicians_NN2 ,_, are_VBR overwhelmed_VVN by_II the_AT amount_NN1 of_IO information_NN1 related_VVN to_II imaging_VVG available_JJ online_JJ from_II diverse_JJ sources_NN2 of_IO questionable_JJ validity_NN1 ,_, and_CC they_PPHS2 are_VBR looking_VVG for_IF help_NN1 ._. 
Informing_VVG patients_NN2 about_II the_AT diagnostic_JJ imaging_NN1 options_NN2 available_JJ as_II31 well_II32 as_II33 the_AT results_NN2 of_IO their_APPGE studies_NN2 is_VBZ a_AT1 crucial_JJ way_NN1 radiologists_NN2 can_VM help_VVI patients_NN2 to_TO be_VBI engaged_VVN in_II their_APPGE care_NN1 as_RG well_RR as_CSA to_TO ensure_VVI that_DD1 critical_JJ results_NN2 are_VBR communicated_VVN to_II them_PPHO2 ._. 
This_DD1 is_VBZ a_AT1 particularly_RR important_JJ opportunity_NN1 because_CS the_AT volume_NN1 of_IO information_NN1 in_II physician_NN1 practices_NN2 and_CC the_AT lack_NN1 of_IO systems_NN2 to_TO manage_VVI the_AT flow_NN1 of_IO information_NN1 sometimes_RT result_VV0 in_II delayed_JJ communication_NN1 of_IO results_NN2 from_II referring_VVG physician_NN1 to_II patients_NN2 ._. 
Radiologists_NN2 can_VM adopt_VVI informatics_NN1 methods_NN2 to_TO manage_VVI the_AT information_NN1 glut_NN1 and_CC can_VM help_VVI communicate_VVI results_NN2 to_II patients_NN2 ._. 
At_II the_AT same_DA time_NNT1 ,_, radiologists_NN2 will_VM become_VVI more_RGR visible_JJ in_II the_AT care_NN1 process_NN1 and_CC make_VV0 patients_NN2 aware_JJ that_CST radiologists_NN2 serve_VV0 an_AT1 integral_JJ role_NN1 in_II patient_NN1 care_NN1 ._. 
A_AT1 second_MD aspect_NN1 of_IO the_AT patient_JJ perspective_NN1 in_II patient-centered_JJ radiology_NN1 is_VBZ enabling_VVG patients_NN2 to_TO participate_VVI in_II the_AT medical_JJ decision-making_JJ process_NN1 ._. 
Just_RR as_RG personalized_JJ medicine_NN1 is_VBZ changing_VVG how_RRQ radiologists_NN2 will_VM approach_VVI interpreting_VVG images_NN2 ,_, shared_JJ decision_NN1 making_NN1 personalizes_VVZ the_AT patient_JJ management_NN1 process_NN1 ._. 
In_II many_DA2 cases_NN2 ,_, the_AT results_NN2 of_IO imaging_NN1 are_VBR suggestive_JJ ,_, but_CCB not_XX conclusive_JJ ,_, for_IF disease_NN1 ._. 
The_AT choice_NN1 of_IO next_MD steps_NN2 (_( additional_JJ imaging_NN1 ,_, biopsy_NN1 ,_, or_CC watchful_JJ waiting_NN1 )_) often_RR is_VBZ affected_VVN by_II patient_JJ preferences_NN2 ._. 
Shared_JJ medical_JJ decision_NN1 making_NN1 permits_VVZ patients_NN2 to_TO weigh_VVI the_AT trade-offs_NN2 in_II their_APPGE utility_NN1 of_IO the_AT different_JJ outcomes_NN2 ,_, the_AT likelihood_NN1 of_IO each_DD1 outcome_NN1 ,_, and_CC their_APPGE risk_NN1 tolerance_NN1 ._. 
At_II the_AT heart_NN1 of_IO the_AT challenges_NN2 for_IF patient-centered_JJ radiology_NN1 from_II both_DB2 the_AT physician_NN1 and_CC patient_JJ perspectives_NN2 is_VBZ accessing_VVG and_CC exploiting_VVG large_JJ amounts_NN2 of_IO information_NN1 ._. 
Physicians_NN2 need_VV0 to_TO access_VVI current_JJ radiologic_JJ knowledge_NN1 to_TO customize_VVI imaging_NN1 and_CC accurately_RR interpret_VVI results_NN2 ._. 
Patients_NN2 need_VV0 access_NN1 to_II radiologic_JJ results_NN2 in_II a_AT1 timely_JJ fashion_NN1 and_CC in_II assessing_VVG the_AT likelihood_NN1 of_IO different_JJ outcomes_NN2 given_VVN these_DD2 results_NN2 ._. 
Informatics_NN1 provides_VVZ methods_NN2 that_CST can_VM enable_VVI patient-centered_JJ radiology_NN1 by_II helping_VVG physicians_NN2 and_CC patients_NN2 manage_VV0 and_CC use_VV0 knowledge_NN1 :_: how_RRQ it_PPH1 is_VBZ acquired_VVN ,_, used_VVD ,_, and_CC moved_VVN around_RP ._. 
From_II the_AT physician_NN1 perspective_NN1 ,_, informatics_NN2 can_VM enable_VVI personalized_JJ medicine_NN1 by_II delivering_VVG guidance_NN1 for_IF examination_NN1 appropriateness_NN1 specific_JJ to_II patients_NN2 and_CC by_II providing_VVG diagnostic_JJ decision_NN1 support_NN1 in_II image_NN1 interpretation_NN1 ._. 
From_II the_AT patient_JJ perspective_NN1 ,_, it_PPH1 can_VM engage_VVI them_PPHO2 more_RGR closely_RR in_II their_APPGE care_NN1 by_II communicating_VVG the_AT results_NN2 of_IO imaging_VVG procedures_NN2 and_CC by_II enabling_VVG shared_JJ decision_NN1 making_VVG ._. 
Diagnostic_JJ decision_NN1 support_NN1 for_IF interpretation_NN1 After_II selecting_VVG the_AT best_JJT radiologic_JJ procedures_NN2 to_TO be_VBI performed_VVN ,_, patient-centered_JJ radiology_NN1 requires_VVZ that_CST the_AT most_RGT accurate_JJ interpretation_NN1 be_VBI rendered_VVN given_VVN the_AT clinical_JJ context_NN1 and_CC observed_VVN radiologic_JJ findings_NN2 ._. 
Radiology_NN1 interpretation_NN1 includes_VVZ three_MC key_JJ steps_NN2 :_: (_( 1_MC1 )_) perception_NN1 of_IO image_NN1 findings_NN2 ,_, (_( 2_MC )_) interpretation_NN1 of_IO those_DD2 findings_NN2 to_TO render_VVI a_AT1 diagnosis_NN1 ,_, and_CC (_( 3_MC )_) decisions_NN2 and_CC recommendations_NN2 about_II patient_JJ management_NN1 (_( next_MD tests_NN2 or_CC treatments_NN2 )_) ._. 
Perception_NN1 of_IO image_NN1 findings_NN2 is_VBZ clearly_RR important_JJ in_II radiology_NN1 ,_, and_CC many_DA2 informatics_NN2 methods_NN2 collectively_RR called_VVN computer-aided_JJ detection_NN1 have_VH0 been_VBN developed_VVN in_II several_DA2 domains_NN2 of_IO radiology_NN1 ._. 
Detecting_VVG image_NN1 features_NN2 is_VBZ approached_VVN in_II a_AT1 similar_JJ manner_NN1 in_II all_DB patients_NN2 ._. 
On_II the_AT other_JJ hand_NN1 ,_, interpretation_NN1 and_CC decision_NN1 making_VVG depend_VVI heavily_RR on_II individual_JJ patient_JJ characteristics_NN2 ,_, and_CC these_DD2 tasks_NN2 should_VM be_VBI supported_VVN in_II patient-centered_JJ radiology_NN1 ._. 
The_AT need_NN1 for_IF assisted_JJ interpretation_NN1 and_CC decision_NN1 making_NN1 is_VBZ underscored_VVN by_II the_AT fact_NN1 that_CST there_EX is_VBZ a_AT1 great_JJ deal_NN1 of_IO variation_NN1 among_II practitioners_NN2 in_II these_DD2 skills_NN2 ._. 
Studies_NN2 have_VH0 documented_VVN interobserver_NN1 disagreement_NN1 in_II the_AT interpretation_NN1 of_IO imaging_VVG studies_NN2 ._. 
For_REX21 example_REX22 ,_, in_II mammography_NN1 interpretation_NN1 ,_, there_EX is_VBZ substantial_JJ variation_NN1 in_II sensitivity_NN1 ,_, specificity_NN1 ,_, and_CC area_NN1 under_II the_AT receiver-operating_JJ characteristic_JJ curve_NN1 among_II radiologists_NN2 ._. 
Much_DA1 of_IO the_AT variation_NN1 in_II practice_NN1 likely_JJ results_NN2 from_II the_AT complexity_NN1 of_IO processing_VVG the_AT vast_JJ amounts_NN2 of_IO knowledge_NN1 needed_VVD to_TO interpret_VVI the_AT myriad_NN1 findings_NN2 observed_VVN in_II imaging_NN1 ._. 
Most_DAT of_IO medical_JJ practice_NN1 is_VBZ not_XX quantitative_JJ but_CCB based_VVN on_II "_" heuristics_NN2 "_" to_TO guide_VVI physicians_NN2 on_II the_AT basis_NN1 of_IO rules_NN2 of_IO thumb_NN1 ._. 
Such_DA heuristics_NN2 can_VM fail_VVI in_II a_AT1 variety_NN1 of_IO circumstances_NN2 when_RRQ combinations_NN2 of_IO features_NN2 related_VVN to_II diagnosis_NN1 do_VD0 not_XX fit_VVI the_AT expected_JJ patterns_NN2 and_CC practitioners_NN2 do_VD0 not_XX recognize_VVI the_AT impact_NN1 of_IO such_DA circumstances_NN2 ._. 
Diagnostic_JJ decision_NN1 support_NN1 is_VBZ an_AT1 informatics_NN1 method_NN1 to_TO help_VVI radiologists_NN2 with_IW image_NN1 interpretation_NN1 and_CC decision_NN1 making_VVG ._. 
Unlike_JJ computer-aided_JJ detection_NN1 ,_, these_DD2 systems_NN2 do_VD0 not_XX focus_VVI on_II detecting_VVG a_AT1 finding_NN1 of_IO interest_NN1 but_CCB rather_RR focus_VV0 on_II the_AT decision_NN1 process_NN1 ,_, specifically_RR on_II the_AT diagnostic_JJ reasoning_NN1 process_NN1 in_II medical_JJ applications_NN2 ._. 
Decision_NN1 support_NN1 systems_NN2 begin_VV0 with_IW the_AT observations_NN2 (_( the_AT findings_NN2 detected_VVN on_II images_NN2 )_) and_CC integrate_VV0 them_PPHO2 with_IW a_AT1 formal_JJ model_NN1 of_IO the_AT knowledge_NN1 used_VMK to_TO make_VVI decisions_NN2 ,_, outputting_VVG the_AT most_RGT likely_JJ diagnoses_NN2 ._. 
Decision_NN1 support_NN1 systems_NN2 incorporate_VV0 patient-specific_JJ knowledge_NN1 ,_, such_II21 as_II22 clinical_JJ histories_NN2 or_CC results_NN2 of_IO other_JJ tests_NN2 ._. 
This_DD1 patient-specific_JJ information_NN1 ,_, in_II combination_NN1 with_IW the_AT imaging_NN1 findings_NN2 ,_, can_VM enable_VVI these_DD2 systems_NN2 to_TO help_VVI radiologists_NN2 render_VVI better_RRR care_VVI that_DD1 is_VBZ personalized_VVN to_II patients_NN2 and_CC reduce_VV0 variation_NN1 in_II practice_NN1 ._. 
In_II most_DAT decision_NN1 support_NN1 applications_NN2 ,_, the_AT computer_NN1 uses_VVZ a_AT1 model_NN1 of_IO disease_NN1 (_( such_II21 as_II22 a_AT1 Bayesian_JJ network_NN1 )_) that_CST incorporates_VVZ image_NN1 features_NN2 and_CC clinical_JJ data_NN as_CSA variables_NN2 in_II the_AT model_NN1 ._. 
The_AT model_NN1 relates_VVZ image_NN1 features_NN2 and_CC other_JJ observed_JJ clinical_JJ parameters_NN2 to_II the_AT likelihood_NN1 of_IO each_DD1 type_NN1 of_IO disease_NN1 ._. 
By_II entering_VVG observed_JJ clinical_JJ and_CC imaging_VVG features_NN2 into_II the_AT model_NN1 ,_, a_AT1 post-test_JJ probability_NN1 of_IO disease_NN1 can_VM be_VBI calculated_VVN and_CC used_VVN to_TO guide_VVI decision_NN1 making_VVG and_CC patient_JJ management_NN1 ._. 
Such_DA decision_NN1 support_NN1 applications_NN2 thus_RR inform_VV0 radiologists_NN2 about_II the_AT most_RGT likely_JJ diseases_NN2 for_IF particular_JJ patients_NN2 ._. 
The_AT input_NN1 to_II decision_NN1 support_NN1 applications_NN2 is_VBZ usually_RR a_AT1 controlled_JJ terminology-encoded_JJ structured_JJ radiology_NN1 report_NN1 ,_, that_DD1 conveys_VVZ the_AT imaging_NN1 observations_NN2 and_CC the_AT key_JJ clinical_JJ information_NN1 to_II the_AT decision_NN1 support_NN1 application_NN1 ._. 
The_AT decision_NN1 support_NN1 application_NN1 uses_VVZ its_APPGE model_NN1 of_IO disease_NN1 to_TO deduce_VVI the_AT best_JJT course_NN1 of_IO action_NN1 and_CC reports_VVZ that_DD1 information_NN1 to_II the_AT practitioner_NN1 ._. 
For_REX21 example_REX22 ,_, a_AT1 decision_NN1 support_NN1 system_NN1 for_IF mammography_NN1 was_VBDZ recently_RR created_VVN to_TO inform_VVI radiologists_NN2 as_II21 to_II22 the_AT likely_JJ diagnosis_NN1 and_CC to_TO provide_VVI guidance_NN1 regarding_II biopsy_NN1 recommendations_NN2 of_IO suspicious_JJ lesions_NN2 ._. 
A_AT1 radiologist_NN1 enters_VVZ the_AT findings_NN2 using_VVG a_AT1 structured_JJ reporting_NN1 form_NN1 ,_, and_CC information_NN1 in_II that_DD1 form_NN1 is_VBZ processed_VVN by_II the_AT system_NN1 to_TO produce_VVI its_APPGE output_NN1 (_( in_II this_DD1 case_NN1 ,_, a_AT1 differential_JJ diagnosis_NN1 ,_, ranked_VVN by_II probability_NN1 of_IO disease_NN1 ;_; )_) ._. 
Discussion_NN1 The_AT core_NN1 aspects_NN2 of_IO patient-centered_JJ radiology_NN1 that_CST informatics_NN2 can_VM support_VVI include_VV0 physician-specific_JJ activities_NN2 (_( selecting_VVG the_AT appropriate_JJ imaging_NN1 procedures_NN2 for_IF particular_JJ patients_NN2 and_CC rendering_VVG the_AT best_JJT imaging_NN1 interpretation_NN1 that_CST is_VBZ personalized_VVN to_II each_DD1 patient_NN1 )_) and_CC patient-specific_JJ interests_NN2 (_( communicating_VVG the_AT results_NN2 of_IO imaging_VVG procedures_NN2 and_CC shared_JJ decision_NN1 making_VVG )_) ._. 
These_DD2 share_VV0 in_RR21 common_RR22 the_AT need_NN1 to_TO manage_VVI large_JJ amounts_NN2 of_IO complex_JJ information_NN1 ,_, a_AT1 task_NN1 that_CST is_VBZ difficult_JJ for_IF people_NN without_IW computer_NN1 assistance_NN1 ._. 
In_II fact_NN1 ,_, in_II current_JJ unaided_JJ practice_NN1 ,_, communication_NN1 sometimes_RT breaks_VVZ down_RP ,_, decisions_NN2 can_VM be_VBI made_VVN without_IW access_NN1 to_II all_DB the_AT necessary_JJ information_NN1 ,_, and_CC there_EX is_VBZ variation_NN1 in_II physician_NN1 practice_NN1 ._. 
The_AT goal_NN1 of_IO informatics_NN1 is_VBZ to_TO bring_VVI the_AT relevant_JJ medical_JJ knowledge_NN1 to_II physicians_NN2 and_CC to_II patients_NN2 to_TO inform_VVI and_CC to_TO help_VVI radiologists_NN2 provide_VVI the_AT best_JJT care_NN1 on_II the_AT basis_NN1 of_IO all_DB patient-specific_JJ information_NN1 and_CC to_TO engage_VVI patients_NN2 in_II their_APPGE health_NN1 care_NN1 delivery_NN1 ._. 
These_DD2 goals_NN2 are_VBR well_RR aligned_VVN with_IW the_AT objectives_NN2 of_IO patient-centered_JJ radiology_NN1 ._. 
A_AT1 few_DA2 core_NN1 informatics_NN1 methodologies_NN2 are_VBR common_JJ to_II several_DA2 patient-centered_JJ radiology_NN1 applications_NN2 ._. 
The_AT first_MD methodology_NN1 is_VBZ controlled_VVN terminology_NN1 ._. 
Creating_VVG and_CC adopting_VVG a_AT1 constrained_JJ list_NN1 of_IO terms_NN2 in_II radiology_NN1 is_VBZ crucial_JJ for_IF clear_JJ communication_NN1 of_IO imaging_VVG results_NN2 to_II referring_VVG physicians_NN2 and_CC patients_NN2 and_CC to_TO permit_VVI computer_NN1 applications_NN2 to_TO process_VVI radiology_NN1 information_NN1 unambiguously_RR ._. 
The_AT second_MD informatics_NN1 methodology_NN1 is_VBZ decision_NN1 support_NN1 ._. 
A_AT1 vast_JJ amount_NN1 of_IO radiology_NN1 knowledge_NN1 currently_RR disseminated_VVN in_II published_JJ articles_NN2 can_VM be_VBI codified_VVN into_II computer_NN1 models_NN2 that_CST can_VM provide_VVI direct_JJ diagnostic_JJ guidance_NN1 to_II radiologist_NN1 for_IF each_DD1 patient_NN1 on_II the_AT basis_NN1 of_IO patient-specific_JJ characteristics_NN2 ._. 
Such_DA applications_NN2 are_VBR the_AT essence_NN1 of_IO translational_JJ medicine_NN1 ,_, by_II applying_VVG the_AT knowledge_NN1 in_II the_AT literature_NN1 to_TO transform_VVI practice_NN1 ._. 
A_AT1 third_MD key_JJ informatics_NN1 methodology_NN1 is_VBZ the_AT electronic_JJ representation_NN1 and_CC transmission_NN1 of_IO information_NN1 ._. 
Radiology_NN1 is_VBZ now_RT an_AT1 all-digital_JJ discipline_NN1 ,_, yet_RR much_DA1 of_IO the_AT work_NN1 flow_NN1 continues_VVZ to_TO adopt_VVI paper-based_JJ and_CC film-based_JJ practice_NN1 ._. 
Such_DA work_NN1 flow_NN1 is_VBZ inefficient_JJ ,_, particularly_RR with_II31 respect_II32 to_II33 communicating_VVG the_AT results_NN2 of_IO imaging_VVG studies_NN2 to_II referring_VVG physicians_NN2 and_CC patients_NN2 ._. 
Patients_NN2 are_VBR increasingly_RR accessing_VVG their_APPGE medical_JJ records_NN2 online_RR ,_, though_CS images_NN2 are_VBR generally_RR not_XX part_NN1 of_IO these_DD2 systems_NN2 ._. 
In_II the_AT future_NN1 ,_, imaging_NN1 will_VM become_VVI part_NN1 of_IO online_JJ health_NN1 information_NN1 systems_NN2 ,_, streamlining_VVG the_AT dissemination_NN1 of_IO imaging_VVG results_NN2 to_II patients_NN2 and_CC referring_VVG physicians_NN2 ,_, and_CC more_RGR closely_RR engaging_JJ patients_NN2 in_II their_APPGE care_NN1 ._. 
Although_CS the_AT informatics_NN1 methods_NN2 discussed_VVN in_II this_DD1 work_NN1 are_VBR promising_VVG in_II enabling_VVG patient-centered_JJ radiology_NN1 ,_, there_EX could_VM be_VBI challenges_NN2 in_II adopting_VVG these_DD2 methods_NN2 ._. 
In_II fact_NN1 ,_, few_DA2 of_IO the_AT informatics_NN1 methods_NN2 described_VVN here_RL are_VBR widely_RR adopted_VVN among_II health_NN1 care_NN1 institutions_NN2 ._. 
The_AT first_MD challenge_NN1 is_VBZ that_DD1 commercial_JJ products_NN2 vary_VV0 in_II their_APPGE features_NN2 and_CC ability_NN1 to_TO deliver_VVI the_AT functionality_NN1 required_VVN ._. 
Most_DAT existing_JJ hospital_NN1 systems_NN2 focus_VV0 on_II storing_VVG and_CC retrieving_VVG patient-specific_JJ information_NN1 ,_, but_CCB few_DA2 incorporate_VV0 additional_JJ knowledge_NN1 such_II21 as_II22 appropriateness_NN1 criteria_NN2 or_CC decision_NN1 support_NN1 models_NN2 ._. 
It_PPH1 is_VBZ often_RR possible_JJ to_TO add_VVI this_DD1 functionality_NN1 by_II working_VVG with_IW the_AT vendors_NN2 ,_, though_CS this_DD1 can_VM be_VBI a_AT1 long_JJ and_CC costly_JJ process_NN1 ._. 
A_AT1 final_JJ challenge_NN1 to_II adopting_VVG informatics_NN1 methods_NN2 for_IF patient-centered_JJ radiology_NN1 is_VBZ cost_NN1 ._. 
Informatics_NN1 systems_NN2 ,_, programming_VVG staff_NN for_IF in-house_JJ solutions_NN2 ,_, and_CC consultation_NN1 and_CC support_VV0 require_VV0 investment_NN1 ,_, both_RR financial_JJ and_CC personnel_NN2 ._. 
In_II the_AT increasingly_RR competitive_JJ radiology_NN1 environment_NN1 ,_, such_DA costs_NN2 could_VM clearly_RR prove_VVI to_TO be_VBI a_AT1 valuable_JJ investment_NN1 by_II enabling_VVG institutions_NN2 to_II more_RGR readily_RR provide_VV0 patient-centered_JJ radiology_NN1 services_NN2 ._. 
In_II the_AT long-term_JJ ,_, as_CSA standards_NN2 and_CC interoperability_NN1 methods_NN2 mature_VV0 ,_, health_NN1 care_NN1 information_NN1 systems_NN2 will_VM likely_RR evolve_VVI to_TO provide_VVI the_AT necessary_JJ informatics_NN1 infrastructure_NN1 for_IF patient-centered_JJ radiology_NN1 ._. 
Just_RR as_CSA Internet_NP1 technologies_NN2 are_VBR now_RT pervasive_JJ in_II most_DAT homes_NN2 and_CC integral_JJ to_II how_RRQ people_NN find_VV0 and_CC use_VV0 information_NN1 ,_, in_II the_AT future_NN1 ,_, informatics_NN1 approaches_NN2 and_CC intelligent_JJ systems_NN2 will_VM also_RR become_VVI pervasive_JJ in_II hospitals_NN2 ,_, being_VBG used_VVN routinely_RR by_II physicians_NN2 and_CC patients_NN2 as_CSA radiology_NN1 becomes_VVZ increasingly_RR patient-centered_JJ ._. 
Conclusion_NN1 Patient-centered_JJ radiology_NN1 is_VBZ an_AT1 important_JJ emerging_JJ paradigm_NN1 that_CST brings_VVZ challenges_NN2 to_II radiologists_NN2 and_CC patients_NN2 as_II31 well_II32 as_II33 opportunities_NN2 for_IF informatics_NN2 ._. 
The_AT core_NN1 methods_NN2 of_IO informatics_NN2 in_II patient-centered_JJ radiology_NN1 help_NN1 physicians_NN2 and_CC patients_NN2 access_VV0 the_AT exploding_JJ amount_NN1 of_IO data_NN ,_, particularly_RR imaging_VVG data_NN ,_, in_II a_AT1 timely_JJ and_CC efficient_JJ manner_NN1 ._. 
Computer_NN1 applications_NN2 will_VM soon_RR be_VBI appearing_VVG to_TO improve_VVI the_AT clarity_NN1 of_IO radiology_NN1 reports_NN2 ,_, to_TO help_VVI deploy_VVI appropriateness_NN1 criteria_NN2 ,_, to_TO help_VVI radiologists_NN2 make_VVI better_JJR decisions_NN2 on_II the_AT basis_NN1 of_IO imaging_VVG information_NN1 ,_, and_CC to_TO enable_VVI patients_NN2 to_TO participate_VVI in_II their_APPGE care_NN1 and_CC in_II shared_JJ decision_NN1 making_VVG ._. 
Informatics_NN1 methods_NN2 are_VBR not_XX futuristic_JJ developments_NN2 on_II the_AT horizon_NN1 ;_; many_DA2 applications_NN2 are_VBR already_RR in_II routine_NN1 use_NN1 in_II several_DA2 institutions_NN2 ._. 
Through_II the_AT adoption_NN1 of_IO these_DD2 methods_NN2 for_IF patient-centered_JJ radiology_NN1 ,_, the_AT value_NN1 radiologists_NN2 bring_VV0 to_II the_AT care_NN1 process_NN1 will_VM be_VBI heightened_VVN ,_, and_CC their_APPGE role_NN1 in_II patient_NN1 care_NN1 will_VM become_VVI more_RGR visible_JJ to_II patients_NN2 ._. 
